{"id":41,"date":"2020-10-09T12:36:49","date_gmt":"2020-10-09T16:36:49","guid":{"rendered":"https:\/\/sites.temple.edu\/ydong\/?page_id=41"},"modified":"2026-03-27T10:54:28","modified_gmt":"2026-03-27T14:54:28","slug":"publication","status":"publish","type":"page","link":"https:\/\/sites.temple.edu\/ydong\/publication\/","title":{"rendered":"Publication"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"41\" class=\"elementor elementor-41\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-14efae59 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"14efae59\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column 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data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-xl\">PUBLICATIONS<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7654b513 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7654b513\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4fb6b211\" data-id=\"4fb6b211\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-717bd98d elementor-tabs-view-horizontal elementor-widget elementor-widget-tabs\" data-id=\"717bd98d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-tabs\">\n\t\t\t<div class=\"elementor-tabs-wrapper\" role=\"tablist\" >\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1901\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"true\" data-tab=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"elementor-tab-content-1901\" aria-expanded=\"false\">PUBLISHED<\/div>\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1902\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"false\" data-tab=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1902\" aria-expanded=\"false\">SUBMITTED<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"elementor-tabs-content-wrapper\" role=\"tablist\" aria-orientation=\"vertical\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"true\" data-tab=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"elementor-tab-content-1901\" aria-expanded=\"false\">PUBLISHED<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1901\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1901\" tabindex=\"0\" hidden=\"false\"><ol style=\"margin-top: 1.5em;margin-bottom: 1.5em\">\n<li style=\"font-size: 16px\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><span style=\"color: var( --e-global-color-text );font-weight: var( --e-global-typography-text-font-weight );font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">Dong, Y. and Li, L. (2026)&nbsp;<\/span><\/span><\/span><\/span><span data-olk-copy-source=\"MessageBody\"><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11424-026-5408-0\" target=\"_blank\" rel=\"noopener\"><u>On marginal coordinate test with multivariate responses<\/u><\/a>. <i>Journal of System Science and Complexity,&nbsp;<\/i><span style=\"font-family: Georgia, sans-serif\">39, 3-16.&nbsp;<\/span>&nbsp;&nbsp;<\/span><\/li>\n<li style=\"font-size: 16px\">Dong, Y., Li, L. and Soale, A, N. (2026) On hybrid principal Hessian directions for sufficient dimension reduction. Navigating Complexity &#8211; Statistical Methods, Data Analysis, and Machine Learning for Actionable Insights (IFCS 2026). Accepted.<\/li>\n<li style=\"text-align: justify;font-size: 16px\"><span style=\"font-size: 22px;font-family: Georgia, sans-serif\"><span style=\"font-size: 16px\">Soale, A. N. and Dong, Y. (2026)&nbsp;<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11424-026-4626-9\" target=\"_blank\" rel=\"noopener\"><u>Envelope dimension reduction with application to binary classification<\/u>.<\/a>&nbsp;<\/span><\/span><i>Journal of System Science and Complexity<span style=\"font-family: Georgia, serif\">,&nbsp;<\/span><\/i><span style=\"font-family: Georgia, sans-serif;text-align: left\">39, 79-87.&nbsp;<\/span><\/li>\n<li style=\"font-size: 16px\"><span data-olk-copy-source=\"MessageBody\">Wen, X., Dong, Y. and Zhu, L. X. (2026)&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167947325001975\" target=\"_blank\" rel=\"noopener\"><u>Multi-population sufficient dimension reduction<\/u><\/a>. <i>Computational Statistics and Data Analysis<\/i>.&nbsp;<span style=\"font-family: Georgia, serif;text-align: justify\">Volume 217, May 2026, 108321.<\/span><\/span><\/li>\n<li style=\"font-size: 16px\">Shen, C., Dong ,Y., Priebe, C., Larson, J., Trinh, H., and Park, Y. (2025)&nbsp;<a style=\"text-align: justify\" href=\"https:\/\/doi.org\/10.1109\/TNSE.2025.3584219\" target=\"_blank\" rel=\"noopener\"><u>Principal graph encoder embedding and principal community detection<\/u><\/a><span style=\"text-align: justify\">.&nbsp;<\/span><span style=\"font-size: 1em;text-align: justify\"><i><span style=\"font-family: Georgia, sans-serif\">IEEE Transactions on Network Science and Engineering<\/span><span style=\"font-family: Georgia, serif\">, <\/span><\/i><span style=\"font-family: Georgia, serif\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">13, 424-437.<\/span><\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><span style=\"font-family: Roboto, sans-serif;font-size: medium\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">Li, Z. and Dong, Y.&nbsp;(2025)&nbsp;<a href=\"https:\/\/doi.org\/10.1080\/10618600.2025.2473936\" target=\"_blank\" rel=\"noopener\"><u>A cepstral model for efficient spectral analysis of covariate-dependent time series<\/u><\/a>.&nbsp;<\/span><\/span><span style=\"font-family: Georgia, serif;text-align: justify\"><i>Journal of Computational and Graphical Statistics<\/i><span style=\"font-family: Roboto, sans-serif\"><span style=\"font-family: Georgia, serif\">,&nbsp;34<\/span><\/span><span style=\"font-family: Georgia, sans-serif;text-align: left\">,&nbsp;1612-1624.<\/span><\/span><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">&nbsp;<\/span><\/span><\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">Shen, C. and Dong, Y. (2025)&nbsp;<a href=\"https:\/\/www.sciltp.com\/journals\/asac\/articles\/2506000739\" target=\"_blank\" rel=\"noopener\"><u>High-dimensional independence testing via maximum and average distance correlations<\/u><\/a>.&nbsp;<i>Applied Statistical Analysis and Computing<\/i><\/span><\/span><\/span><\/span><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">, 1, 100003.<\/span><\/span><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">&nbsp;<\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">Dong, Y. and Li, Z. (2024)&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167715223001712\" target=\"_blank\" rel=\"noopener\"><u>A note on marginal coordinate test in sufficient dimension reduction<\/u><\/a>. <i>Statistics and Probability Letters<\/i><span style=\"font-family: Georgia, serif;text-align: justify\">, Volume 204, January 2024, 109947.<\/span><\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><i style=\"color: var( --e-global-color-text );font-weight: var( --e-global-typography-text-font-weight );font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Kai, B., Huang, M., Yao, W. and Dong, Y.&nbsp;<span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">(2023)<\/span><\/i><\/span><\/span>&nbsp;<a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/10618600.2023.2184374\" target=\"_blank\" rel=\"noopener\"><u>Nonparametric and semiparametric quantile regression via a new MM Algorithm<\/u><\/a>.&nbsp;<\/span><\/i><i style=\"color: var( --e-global-color-text );font-weight: var( --e-global-typography-text-font-weight );font-family: Georgia, serif;text-align: justify\">Journal of Computational and Graphical Statistics<\/i><span style=\"color: var( --e-global-color-text );font-weight: var( --e-global-typography-text-font-weight );font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">, 32, 1613-1623.&nbsp;<\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><i style=\"font-style: normal;font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><span style=\"text-align: justify\"><span style=\"font-family: Georgia, serif\"><i><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Dong, Y.,&nbsp;<\/span><\/i><\/span><\/span><\/span><\/i><\/span><\/i><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\">Soale, A. N. and&nbsp;<\/span><\/span><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Power, M.&nbsp;(2023)&nbsp;<\/span><\/i><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0378375823000150\" target=\"_blank\" rel=\"noopener\"><u>A selective review of sufficient dimension reduction for multivariate response regression<\/u><\/a>.&nbsp;<i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><i style=\"font-family: Georgia, sans-serif;text-align: left\">Journal of Statistical Planning and Inference<\/i><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">, 226,<\/span><\/i><\/span><\/i><\/span><\/i>&nbsp;63-70.<br><\/span><\/i><\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><i style=\"font-style: normal;font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Soale, A. N. and Dong, Y. (2022)&nbsp;<\/span><\/i><span style=\"font-style: normal;font-size: medium\"><a href=\"https:\/\/doi.org\/10.1080\/10485252.2021.2025237\" target=\"_blank\" rel=\"noopener\"><u>On sufficient dimension reduction via&nbsp;principal asymmetric least squares<\/u><\/a>. <\/span><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><span style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;text-align: left\"><i style=\"font-style: normal\">Journal of Nonparametric Statistics, <\/i>34, 77-94.<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><br><\/span><\/span><\/li>\n<li style=\"font-size: 16px\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Zhou, T., Dong, Y. and Zhu, L. P. (2021)&nbsp;<u><a href=\"https:\/\/www.doi.org\/10.5705\/ss.202019.0095\" target=\"_blank\" rel=\"noopener\">Testing the linear mean and constant variance conditions in sufficient dimension reduction<\/a>.<\/u>&nbsp;<i>Statistica Sinica<\/i>,&nbsp;<\/span><\/i><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">31, 2179-2194.&nbsp;<\/span><\/i><\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Power, M. and Dong, Y.&nbsp;<\/span><\/i><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><span style=\"font-family: Georgia, serif;text-align: justify\">(2021)&nbsp;<\/span><\/span><\/i><\/span><\/i><\/span><\/i><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167715221000651\" target=\"_blank\" rel=\"noopener\"><u>Bayesian model averaging sliced inverse regression<\/u><\/a>. <\/span><\/i><i style=\"font-family: Georgia, sans-serif\">Statistics and Probability Letters<\/i><span style=\"font-family: Georgia, serif;text-align: justify\">, Volume 174, July 2021, 109103.<\/span>&nbsp;<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Soale, A. N. and Dong, Y. (2021)&nbsp;<\/span><\/i><span style=\"font-size: medium\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S037837582030118X\" target=\"_blank\" rel=\"noopener\"><u>On expectile-assisted inverse regression estimation for sufficient dimension reduction<\/u><\/a>.&nbsp;<\/span><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><i style=\"font-family: Georgia, sans-serif;text-align: left\">Journal of Statistical Planning and Inference<\/i><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">, 213, 80-92.<\/span><\/i><\/span><\/i><\/span><\/i><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><span style=\"text-align: justify\"><span style=\"font-family: Georgia, serif\"><i><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Dong, Y. (2021)&nbsp;<\/span><\/i><\/span><\/span>Sufficient dimension reduction through independence and conditional mean independence measures.&nbsp; Springer Nature Switzerland AG,&nbsp;<\/span><\/i><\/span><\/i>Efstathia Bura and Bing Li (eds.),&nbsp;<i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><a href=\"https:\/\/www.springer.com\/gp\/book\/9783030690083#aboutBook\" target=\"_blank\" rel=\"noopener\"><u>Festschrift in Honor of R. Dennis Cook<\/u><\/a>.<\/span><\/i><\/span><\/i><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><span style=\"font-family: Georgia, serif;text-align: justify\">Li, Z. and Dong, Y. (2021)&nbsp;<\/span><a style=\"font-family: Georgia, serif;text-align: justify\" href=\"https:\/\/doi.org\/10.1080\/10618600.2020.1806854\" target=\"_blank\" rel=\"noopener\"><u>Model-free variable selection with matrix-valued predictors<\/u>.<\/a><span style=\"font-family: Georgia, serif;text-align: justify\">&nbsp;<\/span><i style=\"font-family: Georgia, serif;text-align: justify\">Journal of Computational and Graphical Statistics<\/i><\/span><\/i><\/span><\/i><\/span><\/i><\/span><\/i><\/span><\/i><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">, 30, 171-181.&nbsp;<\/span><\/i><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-size: medium\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, serif\"><i><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Ar<\/span><\/i><\/span><\/i><\/span><\/span><\/span><\/span><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">temiou, A.,<\/span><\/i><\/span><\/i>&nbsp;Dong, Y. and Shin, S. J. (2021)&nbsp;<a href=\"https:\/\/doi.org\/10.1016\/j.patcog.2020.107768\" target=\"_blank\" rel=\"noopener\"><u>Real-time sufficient dimension reduction through principal least squares support vector machines<\/u><\/a>. <\/span><span style=\"font-family: Georgia, sans-serif;text-align: left\">Pattern Recognition<\/span><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">, Volume 112, April 2021, 107768.<\/span><\/i><\/span><\/i><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Dong, Y. (2021)&nbsp;<\/span><a style=\"background-color: #ffffff;font-family: Georgia, sans-serif;font-style: normal;text-align: left\" href=\"https:\/\/doi.org\/10.1016\/j.jspi.2020.06.006\" target=\"_blank\" rel=\"noopener\"><u>A brief review of linear sufficient dimension reduction through optimization<\/u><\/a><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">. <\/span><i style=\"font-family: Georgia, sans-serif;text-align: left\">Journal of Statistical Planning and Inference<\/i><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">, 211, 154-161.&nbsp;&nbsp;<\/span><\/i><\/span><\/i><\/span><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><i style=\"font-family: Georgia, serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Dong, Y., Yu, Z. and Zhu, L. P. (2020)&nbsp;<span style=\"font-size: 1em\"><span style=\"font-size: 1em\"><a href=\"https:\/\/doi.org\/10.1016\/j.csda.2020.107042\" target=\"_blank\" rel=\"noopener\"><u>Model-free variable selection for conditional mean in regression<\/u><\/a>. <i>Computational Statistics and Data Analysis<\/i><\/span><\/span>, 152, 107042.&nbsp;<br><\/span><\/i><\/span><\/i><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><i style=\"font-family: Georgia, serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Power, M. and Dong, Y. (2020)&nbsp;<\/span><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><a href=\"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/24754269.2020.1829394\" target=\"_blank\" rel=\"noopener\">Comment on &#8220;Review of sparse sufficient dimension reduction&#8221;<\/a>&nbsp;by Li, L., Wen, X. and Yu, Z.&nbsp;<\/span><span style=\"font-family: Georgia, sans-serif;text-align: left\">Statistical Theory and Related Fields<\/span><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">, 4, 149-150.&nbsp;&nbsp;<\/span><\/i><br><\/span><\/i><\/span><\/i><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Tang, C., Fang, X. and Dong, Y. (2020)&nbsp;<u><a href=\"https:\/\/jmlr.org\/papers\/volume21\/19-071\/19-071.pdf\" target=\"_blank\" rel=\"noopener\">High-dimensional interactions detection with sparse principal Hessian matrix<\/a>.<\/u>&nbsp;<i>Journal of Machine Learning Research<\/i>, 21 (19), 1-25.<\/span><\/i><\/span><\/i><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Shen, C., Chen, L., Dong, Y. and Priebe, C. E. (2020)&nbsp;<a style=\"background-color: #ffffff\" href=\"https:\/\/doi.org\/10.1109\/TIT.2020.2981309\" target=\"_blank\" rel=\"noopener\"><u>Sparse representation classification beyond L1 minimization and the subspace assumption<\/u><\/a>.&nbsp;<i>IEEE Transactions on Information Theory<\/i>, 66 (8), 5061-5071.<\/span><\/i><\/span><\/i><br><\/span><\/i><\/span><\/i><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><i style=\"font-family: Georgia, serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Alothman, A., Dong, Y. and Artemiou, A. (2018)&nbsp;<a style=\"background-color: #ffffff\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X18300174\" target=\"_blank\" rel=\"noopener\"><u>On dual model-free variable selection with two groups of variables<\/u><\/a>.&nbsp;<i>Journal of Multivariate Analysis<\/i>, 167, 366-377.<br><\/span><\/i><\/span><\/i><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><i style=\"font-family: Georgia, serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Bharadwaj, N. and Dong, Y. (2018) Comment on &#8220;<u><a href=\"https:\/\/rss.onlinelibrary.wiley.com\/doi\/full\/10.1111\/rssa.12315\" target=\"_blank\" rel=\"noopener\">Statistical challenges of administrative and transaction data<\/a>&#8221;<\/u>&nbsp;by Hand, D. J.&nbsp;<i>Journal of the Royal Statistical Society, Series A<\/i>, 181, 555-605.<\/span><\/i><\/span><\/i><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><i style=\"font-family: Georgia, serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Dong, Y. and Li, Z. (2018)&nbsp;<a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/10485252.2018.1508677?journalCode=gnst20\" target=\"_blank\" rel=\"noopener\"><u>On sliced inverse regression with missing values<\/u><\/a>.<i>&nbsp;Journal of Nonparametric Statistics<\/i><u>,<\/u>&nbsp;30, 990-1002.<\/span><\/i><\/span><\/i><br><\/span><\/i><\/span><\/i><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\"><i style=\"font-family: Georgia, serif;font-size: 16px;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\"><i style=\"font-family: Georgia, serif;text-align: justify\"><span style=\"font-family: Georgia, sans-serif;font-style: normal;text-align: left\">Dong, Y., Xia, Q., Tang, C. and Li, Z. (2018)&nbsp;<a style=\"background-color: #ffffff\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167947318300951\" target=\"_blank\" rel=\"noopener\"><u>On sufficient dimension reduction with missing responses through estimating equations<\/u><\/a>.&nbsp;<i>Computational Statistics and Data Analysis<\/i>, 126, 67-77.<br><\/span><\/i><\/span><\/i><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Dong, Y. and Zhang, Y. (2018)&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167715218301391\" target=\"_blank\" rel=\"noopener\"><u>On a new class of sufficient dimension reduction estimators<\/u><\/a>.&nbsp;<i>Statistics and Probability Letters<\/i>, 139, 90-94.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Bharadwaj, N., Noble, C., Tower, A., Smith, L. and Dong, Y. (2017)&nbsp;<a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/jpim.12404\" target=\"_blank\" rel=\"noopener\"><u>Predicting innovation success in the motion picture industry: the influence of multiple quality signals<\/u><\/a>.&nbsp;<i>Journal of Product Innovation Management<\/i>, 34, 659-680.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Dong, Y., Kai, B. and Yu, Z. (2017)&nbsp;<a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/00949655.2016.1205067?journalCode=gscs20\" target=\"_blank\" rel=\"noopener\"><u>Dimension reduction via local rank regression<\/u><\/a>.&nbsp;<i>Journal of Statistical Computation and Simulati<\/i>on, 87, 239-249.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Xia, Q. and Dong, Y. (2017)&nbsp;<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11424-017-6227-0\" target=\"_blank\" rel=\"noopener\"><u>On a new hybrid estimator for the central mean space<\/u><\/a>.&nbsp;<i>Journal of Systems Science and Complexity<\/i>, 30, 111-121.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Artemiou, A. and Dong, Y. (2016)&nbsp;<a href=\"https:\/\/projecteuclid.org\/euclid.ejs\/1459967423\" target=\"_blank\" rel=\"noopener\"><u>Sufficient dimension reduction via principal Lq support vector machine<\/u><\/a>.&nbsp;<i>Electronic Journal of Statistics<\/i>, 10, 783-805.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Dong, Y. (2016)&nbsp;<a href=\"https:\/\/www.intlpress.com\/site\/pub\/pages\/journals\/items\/sii\/content\/vols\/0009\/0002\/a002\/\" target=\"_blank\" rel=\"noopener\"><u>A note on moment-based sufficient dimension reduction estimators<\/u><\/a>.&nbsp;<i>Statistics and Its Interface<\/i>, 9, 141-145.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Dong, Y. and Yang, C. (2016)&nbsp;<a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/15598608.2015.1095136?journalCode=ujsp20\" target=\"_blank\" rel=\"noopener\"><u>Cluster-based least absolute deviation regression for dimension reduction<\/u><\/a>.&nbsp;<i>Journal of Statistical Theory and Practice<\/i>, 10, 121-132.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Dong, Y., Yang, C. and Yu, Z. (2016)&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X16000634\/\" target=\"_blank\" rel=\"noopener\"><u>On permutation tests for predictor contribution in sufficient dimension reduction<\/u><\/a>.&nbsp;<i>Journal of Multivariate Analysis<\/i><a href=\"http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X16000634\/\" target=\"_blank\" rel=\"noopener\">,<\/a>&nbsp;149, 81-91.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Yu, Z. and Dong, Y. (2016)&nbsp;<u><a href=\"http:\/\/www3.stat.sinica.edu.tw\/statistica\/J26N3\/J26N313\/J26N313.html\" target=\"_blank\" rel=\"noopener\">Model-free coordinate test and variable selection via directional regression.<\/a>&nbsp;<\/u><i>Statistica Sinica<\/i>, 26, 1159-1174.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Yu, Z., Dong, Y. and Shao, J. (2016)&nbsp;<a href=\"https:\/\/projecteuclid.org\/euclid.aos\/1479891629\" target=\"_blank\" rel=\"noopener\"><u>On marginal sliced inverse regression for ultrahigh dimensional model-free feature selection<\/u><\/a>.<i>&nbsp;The Annals of Statistics<\/i><u>,<\/u>&nbsp;44, 2594-2623.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p>Yu, Z., Dong, Y. and Zhu, L. X. (2016)&nbsp;<a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/01621459.2015.1050494\" target=\"_blank\" rel=\"noopener\"><u>Trace pursuit: a general framework for model-free variable selection<\/u>.<\/a>&nbsp;<i>Journal of the American Statistical Association<\/i>, 111, 813-821.<\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span lang=\"EN-US\" style=\"font-size: 10pt;font-family: Cambria, serif\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">Dong, Y. and Yu, Z. (2015)&nbsp;<\/span><a style=\"font-family: Georgia, sans-serif;font-size: 16px\" href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-42571-9_4\" target=\"_blank\" rel=\"noopener\"><u>Direction estimation in a general regression model with discrete predictors<\/u><\/a><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">.&nbsp; In: Jin Z., Liu M., Luo X. (eds) New Developments in Statistical Modeling, Inference and Application. ICSA Book Series in Statistics. Springer, Cham.<\/span><br><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span lang=\"EN-US\" style=\"font-size: 10pt;font-family: Cambria, serif\">&nbsp;<\/span><span style=\"font-size: 1em\">Dong, Y., Yu, Z. and Zhu, L. P. (2015)&nbsp;<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X14002322\" target=\"_blank\" rel=\"noopener\"><u>Robust inverse regression for dimension reduction<\/u><\/a>.&nbsp;<i>Journal of Multivariate Analysi<\/i>s, 134, 71-81.<\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">Bharadwaj, N. and Dong, Y. (2014)&nbsp;<\/span><a style=\"font-family: Georgia, sans-serif;font-size: 16px\" href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/jpim.12124\" target=\"_blank\" rel=\"noopener\"><u>Towards further understanding the market sensing capability-value creation relationship<\/u><\/a><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">.<\/span><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">&nbsp;<\/span><i style=\"font-family: Georgia, sans-serif;font-size: 16px\">Journal of Product Innovation Management<\/i><u style=\"font-family: Georgia, sans-serif;font-size: 16px\">,<\/u><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">&nbsp;<\/span><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">31, 799\u2013813.<\/span><br><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\">Yu, Z., Dong, Y. and Huang, M. (2014)&nbsp;<\/span><a style=\"font-size: 16.6px;font-family: serif\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X13002236\" target=\"_blank\" rel=\"noopener\"><u>General directional regression<\/u><\/a><span style=\"font-size: 16.6px;font-family: serif\">.<\/span><span style=\"font-size: 16.6px;font-family: serif\">&nbsp;<\/span><i style=\"font-size: 16.6px;font-family: serif\">Journal of Multivariate Analysis<\/i><span style=\"font-size: 16.6px;font-family: serif\">, 124, 94-104.<\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\">Dong, Y., Yu, Z. and Sun, Y. (2013)&nbsp;<\/span><u style=\"font-size: 16.6px;font-family: serif\"><a href=\"https:\/\/www.intlpress.com\/site\/pub\/pages\/journals\/items\/sii\/content\/vols\/0006\/0001\/a005\/index.php\" target=\"_blank\" rel=\"noopener\">A note on&nbsp;robust&nbsp;kernel inverse regression<\/a>.<\/u><span style=\"font-size: 16.6px;font-family: serif\">&nbsp;<\/span><i style=\"font-size: 16.6px;font-family: serif\">Statistics and Its Interface<\/i><span style=\"font-size: 16.6px;font-family: serif\">, 6, 45-52.<\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">Dong, Y. and Zhu, L. P. (2013)<\/span><a style=\"font-family: Georgia, sans-serif;font-size: 16px\" href=\"https:\/\/www.intlpress.com\/site\/pub\/pages\/journals\/items\/sii\/content\/vols\/0006\/0003\/a008\/index.php\" target=\"_blank\" rel=\"noopener\">&nbsp;<u>Direction estimation in single-index model with missing values<\/u>.<\/a><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">&nbsp;<\/span><i style=\"font-family: Georgia, sans-serif;font-size: 16px\">Statistics and Its Interface<\/i><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">, 6, 379-385.<\/span><br><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: Georgia, sans-serif;font-size: 16px\"><span style=\"text-indent: -0.25in;font-size: 16.6px;font-family: serif\">Yu, Z., Dong, Y. and Guo, R. (2013)&nbsp;<\/span><span style=\"text-indent: -0.25in;font-family: serif\"><span style=\"font-size: 16.6px\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0167715212004816?via%3Dihub\" target=\"_blank\" rel=\"noopener\"><u>On determining the structural dimension via directional regression<\/u>.<\/a>&nbsp;<\/span><\/span><i style=\"text-indent: -0.25in;font-size: 16.6px;font-family: serif\">Statistics &amp; Probability Letters<\/i><span style=\"text-indent: -0.25in;font-size: 16.6px;font-family: serif\">, 83, 987-992.<\/span><br><\/span><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: medium\">Zhu, L. P., Dong, Y. and Li, R. (2013)&nbsp;<\/span><a style=\"font-size: medium\" href=\"http:\/\/www3.stat.sinica.edu.tw\/statistica\/oldpdf\/A23n312.pdf\" target=\"_blank\" rel=\"noopener\"><u>Semiparametric estimation of conditional heteroscedasticity via single-index modeling<\/u><\/a><span style=\"font-size: medium\">.<\/span><span style=\"font-size: medium\">&nbsp;<\/span><i style=\"font-size: medium\">Statistica Sinica<\/i><span style=\"font-size: medium\">, 23, 1235-1255.<\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: medium\">Dong, Y. and Yu, Z. (2012)&nbsp;<\/span><a style=\"font-size: medium\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X12001479\" target=\"_blank\" rel=\"noopener\"><u>Dimension reduction for the conditional kth moment via central solution space<\/u><\/a><span style=\"font-size: medium\">.&nbsp;<\/span><i style=\"font-size: medium\">Journal of Multivariate Analysis<\/i><span style=\"font-size: medium\">, 112, 207-218.<\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: medium\">Dong, Y. and Zhu, L. P. (2012)&nbsp;<a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/sam.10132\" target=\"_blank\" rel=\"noopener\"><u>A note on sliced inverse regression with missing predictors<\/u><\/a>.&nbsp;<i>Statistical Analysis and Data Mining<\/i>, 5, 128-138.&nbsp;<br><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: medium\"><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-size: medium\">Dong, Y. and Li, B. (2010)&nbsp;<\/span><\/span><a style=\"font-family: Georgia, sans-serif;font-size: 16px\" href=\"https:\/\/academic.oup.com\/biomet\/article-abstract\/97\/2\/279\/219362?redirectedFrom=fulltext\" target=\"_blank\" rel=\"noopener\"><u>Dimension reduction for non-elliptically distributed predictors: second-order methods.<\/u><\/a><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">&nbsp;<\/span><i style=\"font-family: Georgia, sans-serif;font-size: 16px\">Biometrika<\/i><span style=\"font-family: Georgia, sans-serif;font-size: 16px\">, 97, 279-294.<\/span><br><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: medium\">Dong, Y. and Zhu, L. P. (2010)&nbsp;<u><a href=\"http:\/\/www3.stat.sinica.edu.tw\/statistica\/j20n3\/j20n31\/j20n31.html\" target=\"_blank\" rel=\"noopener\">Comment on &#8220;Envelope models for parsimonious and efficient multivariate linear regression<\/a>&#8221;<\/u>.&nbsp;<i>Statistica Sinica<\/i>, 20, 993-995.<br><\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: medium\">Yu, Z., Dong, Y. and Fang, Y. (2010)&nbsp;<a href=\"http:\/\/aps.ecnu.edu.cn\/EN\/abstract\/abstract8655.shtml\" target=\"_blank\" rel=\"noopener\"><u>Marginal coordinate tests for central mean subspace with principal Hessian directions.<\/u><\/a>&nbsp;<i>Chinese Journal of Applied Probability and Statistics<\/i>, 26, 544-552.<\/span><\/p>\n<\/li>\n<li style=\"font-size: 16px\">\n<p><span style=\"font-size: 16.6px;font-family: serif\"><span style=\"font-family: 'Times New Roman';font-size: medium\">Li, B. and Dong, Y. (2009)&nbsp;<u><a href=\"http:\/\/DOI.org\/10.1214\/08-AOS598\">Dimension reduction for non-elliptically distributed predictors.<\/a><\/u>&nbsp;<i>The Annals of Statistics<\/i>, 37, 1272-1298.<\/span><\/span><\/p>\n<\/li>\n<\/ol><\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"false\" data-tab=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1902\" aria-expanded=\"false\">SUBMITTED<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1902\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1902\" tabindex=\"0\" hidden=\"hidden\"><ul><li><span style=\"font-size: medium\">Chen, C., Dong, Y., and Soale, A. N.\u00a0A concave pairwise fusion approach to multiresponse regression clustering. Submitted.<\/span><\/li><li><span style=\"font-size: medium\">Dong, Y.\u00a0 A review of ordinary least squares and its variations for sufficient dimension reduction. Submitted.<\/span><\/li><li><span style=\"font-size: medium\">Shen, C. and Dong, Y. Linear discriminant analysis with gradient optimization on covariance inverse. Submitted.<\/span><\/li><li><span style=\"font-size: medium\">Shen, C. and Dong, Y. A graph sufficiency perspective for neural networks. Submitted.\u00a0<\/span><\/li><li><span style=\"font-size: medium\">Shen, C, Dong, Y. and Priebe. C. E. Decision tree embedding by leaf-means. Submitted.\u00a0\u00a0<\/span><\/li><li><span style=\"font-size: medium\">Li, L, Lei, J., and Dong, Y. Probabilistic win ratio method for hierarchical composite endpoints with coarsened outcomes. Submitted.<\/span><\/li><li><span style=\"font-size: medium\">Dong, Y., Wen, X. and Zhu, L. X. Layered sufficient dimension reduction for binary response modelling and visualization. Submitted.<\/span><\/li><li><span style=\"font-size: medium\">Dong, Y. and Li, L. A common subspace approach to multi-population central mean subspace estimation. Submitted.\u00a0<\/span>\u00a0<\/li><\/ul><\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cfe6c27 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cfe6c27\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2804402c\" data-id=\"2804402c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2bb2a30c elementor-widget elementor-widget-heading\" data-id=\"2bb2a30c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Address:1810 Liacouras Walk, Room 382 Philadelphia PA, 19122\n\n<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6d6cd948 elementor-widget elementor-widget-heading\" data-id=\"6d6cd948\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Phone: 215-204-6878\n\n<br><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3ff5817 elementor-widget elementor-widget-heading\" data-id=\"3ff5817\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Email: ydong@temple.edu<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>About me Publications Facebook Twitter Google-plus PUBLICATIONS PUBLISHED SUBMITTED PUBLISHED Dong, Y. and Li, L. (2026)&nbsp;On marginal coordinate test with multivariate responses. Journal of System Science and Complexity,&nbsp;39, 3-16.&nbsp;&nbsp;&nbsp; Dong, Y., Li, L. and Soale, A, N. (2026) On hybrid principal Hessian directions for sufficient dimension reduction. Navigating Complexity &#8211; Statistical Methods, Data Analysis, and &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/sites.temple.edu\/ydong\/publication\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Publication&#8221;<\/span><\/a><\/p>\n","protected":false},"author":24051,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_canvas","meta":{"footnotes":""},"class_list":["post-41","page","type-page","status-publish","hentry","entry"],"_links":{"self":[{"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/pages\/41","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/users\/24051"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/comments?post=41"}],"version-history":[{"count":147,"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/pages\/41\/revisions"}],"predecessor-version":[{"id":434,"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/pages\/41\/revisions\/434"}],"wp:attachment":[{"href":"https:\/\/sites.temple.edu\/ydong\/wp-json\/wp\/v2\/media?parent=41"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}