{"id":8,"date":"2014-09-05T20:26:50","date_gmt":"2014-09-05T20:26:50","guid":{"rendered":"https:\/\/sites.temple.edu\/yongtang\/?page_id=8"},"modified":"2025-03-09T22:41:35","modified_gmt":"2025-03-10T02:41:35","slug":"research","status":"publish","type":"page","link":"https:\/\/sites.temple.edu\/yongtang\/research\/","title":{"rendered":"Research"},"content":{"rendered":"<p><strong>Research Grants:<\/strong><\/p>\n<ul>\n<li>PI, a Subaward of NIH R01GM140476<\/li>\n<li>Co-PI, NSF <a href=\"https:\/\/www.nsf.gov\/awardsearch\/showAward?AWD_ID=2210687&amp;HistoricalAwards=false\">DMS-2210687<\/a><\/li>\n<li>PI, NSF <a href=\"http:\/\/www.nsf.gov\/awardsearch\/showAward?AWD_ID=1546087&amp;HistoricalAwards=false\">IIS-1546087.<\/a><\/li>\n<li>PI, NSF <a href=\"http:\/\/www.nsf.gov\/awardsearch\/showAward?AWD_ID=1533956&amp;HistoricalAwards=false\">SES-1533956.<\/a><\/li>\n<\/ul>\n<p><strong>Research interests<\/strong>: methods, theory, and applications in statistics and data science:<\/p>\n<ul>\n<li>Empirical likelihood<\/li>\n<li>Longitudinal and dependent data analysis<\/li>\n<li>High-dimensional data analysis<\/li>\n<li>Financial statistics and econometrics<\/li>\n<li>Sampling statistics and analysis of missing data<\/li>\n<li>Nonparametric and semiparametric statistical methods<\/li>\n<\/ul>\n<p><strong>Publications:<\/strong><\/p>\n<p><strong>2025<\/strong><\/p>\n<ul>\n<li>Rilling, J. and Tang, C.Y. (2025). A new <em>p<\/em>-value based multiple testing procedure for generalized linear models. <em>Statistics and Computing. To appear.\u00a0<\/em><\/li>\n<li>Chang, J., Tang, C.Y., and Zhu, Y. (2025). Bayesian penalized empirical likelihood and MCMC sampling. <em>Journal of the Royal Statistical Society: Series B. To appear.<\/em><\/li>\n<li>Wang, Y., Tong, J., Hu, X., Ye, Z.-S., Tang, C.Y., and Chen, Y. (2025). Semiparametric sieve estimation for survival data with two-layer censoring. <em>Biometrika. To appear.<\/em><\/li>\n<li>Tong, P., Chen, S.X., and Tang, C.Y. (2025). Multivariate calibrations with auxiliary variables. <em>Statistica Sinica. To appear.<\/em><\/li>\n<\/ul>\n<p><strong>2024<\/strong><\/p>\n<ul>\n<li>Chang, J., Hu, Q., Liu, C., and Tang, C.Y. (2024). Optimal covariance matrix estimation for high-dimensional noise in high-frequency data. <em>Journal of Econometrics. 239, 105329.<\/em><\/li>\n<li>Jiang, B., Liu, C., and Tang, C.Y. (2024). <span dir=\"ltr\" role=\"presentation\">Dynamic covariance matrix estimation and portfolio <\/span><span dir=\"ltr\" role=\"presentation\">analysis with high-frequency data.<em> Journal of Financial Econometrics. 22, 461-491. <\/em><\/span><\/li>\n<li>Chen, D., Li, C., Tang, C.Y., and Yan, J. (2024). The leverage effect puzzle under semi-nonparametric stochastic volatility models. <em>Journal of Business &amp; Economic Statistics. 42, 548-562.<\/em><\/li>\n<li>Tang, C.Y. (2024). A model specification test for semiparametric nonignorable missing data modeling.\u00a0 <em>Econometrics and Statistics. 30, 124-132.<br \/>\n<\/em><\/li>\n<li>Hu, J., Chen, Y., Leng, C., and Tang, C.Y. (2024). Applied regression analysis of correlations for correlated Data. <em>Annals of Applied Statistics. 18,184-198.<\/em><\/li>\n<li>Duan, R., Liang, C. J., Shaw, P., Tang, C. Y., and Chen, Y. (2024). Testing the missing at random assumption in generalized linear models in the presence of instrumental variables. <em>Scandinavian Journal of Statistics. 51, 334-354.<\/em><\/li>\n<\/ul>\n<p><strong>2023<\/strong><\/p>\n<ul>\n<li>Jing, N., Fang, E.X., and Tang, C.Y. (2023). Robust matrix estimations meet Frank-Wolf algorithms. <em>Machine Learning. 112, 2723-2760.<\/em><\/li>\n<li>Zhang, W., Li, Y., Chen, Y., and Tang, C.Y. (2023). Parsimonious Gaussian copula modelling through constrained Cholesky decomposition for data with temporal dependence. <em>Scientia Sinica Mathematica. 53, 777-790.<\/em><\/li>\n<li>Guo, X., Chen, Y., and Tang, C.Y. (2023).\u00a0 Information criteria for latent factor models: a study on factor pervasiveness and adaptivity. <em>Journal of Econometrics. 233, 237-250.<\/em><\/li>\n<\/ul>\n<p><strong>2022<\/strong><\/p>\n<ul>\n<li>Sarkar, S.K. and Tang, C.Y. (2022). Adjusting the Benjamini-Hochberg method for controlling the false discovery rate in knockoff assisted variable selection.<em> Biometrika. 109, 1149-1155.<\/em><\/li>\n<li>Sun, N. and Tang, C.Y. (2022).\u00a0 Testing high-dimensional covariance matrices with random projections and corrected likelihood ratio.\u00a0 <em>Statistics and Its Interface. 15, 449-461. <\/em><\/li>\n<li>Tong, P., Chen, S.X., and Tang, C.Y. (2022). Detecting and evaluating dust-events in north China with ground air quality data. <em>Earth and Space Science. 9, e2021EA001849. <\/em><\/li>\n<li>Yin, Z., Tong, J., Chen, Y., Hubbard, R.A., and Tang, C.Y. (2022). A cost-effective chart review sampling design to account for phenotyping Error in EHR data. <em>Journal of the American Medical Informatics Association. 29, 52-61.<\/em><\/li>\n<\/ul>\n<p><strong>2021<\/strong><\/p>\n<ul>\n<li>Ye, Z.,\u00a0 Li, X., and Tang, C.Y. (2021). Nonparametric inference for superposed renewal processes with applications in parametric inferences. <em>Bernoulli. 27, 2804-2826.<br \/>\n<\/em><\/li>\n<li>Guo, X. and Tang, C.Y. (2021). Specification tests for covariance structures in high-dimensional statistical models. <em>Biometrika. 108, 335-351.<br \/>\n<\/em><\/li>\n<li>Chang, J., Chen, S. X., Tang, C.Y., and Wu, T.T. (2021). High-dimensional empirical likelihood inference. <em>Biometrika. 108, 127-147.<br \/>\n<\/em><\/li>\n<\/ul>\n<p><strong>2020<\/strong><\/p>\n<ul>\n<li>Bruce, S.A., Tang, C.Y., Hall, M. H., and Krafty, R.T. (2020). Empirical frequency band analysis of nonstationary time series. <em>Journal of the American <\/em><em>Statistical Association, Theory and Methods<\/em>. <em>115, 1933-1945<\/em><em>.<\/em><\/li>\n<li>Tang, C.Y., Fang, E.X., and Dong, Y. (2020). High-dimensional interactions detection with sparse principal hessian matrix. <em>Journal of Machine Learning Research<\/em>. <em>21(19) 125<\/em>.<\/li>\n<li>Tang, C.Y., Fan, Y., and Kong, Y. (2020). Precision matrix estimation by inverse principal orthogonal decomposition. <em>Communications in Mathematical Research. 36 68-92.\u00a0 <\/em><\/li>\n<\/ul>\n<p><strong>2019<\/strong><\/p>\n<ul>\n<li>Tang, C.Y., Zhang, W., and Leng, C. (2019). Discrete longitudinal data modeling with a mean-correlation regression approach.<em> Statistica Sinica. 29, 853-876.<\/em><\/li>\n<\/ul>\n<p><strong>2018<\/strong><\/p>\n<ul>\n<li>Yuan, M., Tang, C.Y., Hong, Y., and Yang, J. (2018). Disentangling and assessing uncertainties in multiperiod corporate default risk predictions. <em>Annals of Applied Statistics. 12, 2587-2617.<\/em><\/li>\n<li>Chang, J., Tang, C.Y., and Wu, T.T. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations. <em>Annals of Statistics. 46, 3185-3216.<\/em><\/li>\n<li>Chang, J.,Guo, J., and Tang, C.Y. (2018). Peter Hall\u2019s contribution to empirical likelihood. S<em>tatistica Sinica. 28,2375-2387.<\/em><\/li>\n<li>Dong, Y., Xia, Q., Tang, C.Y., and Li, Z. (2018). On Sufficient Dimension Reduction with Missing Responses through Estimating Equations.<em> Computational Statistics and Data Analysis. 126,67-77.<\/em><\/li>\n<li>Chang, J., Delaigle, A., Hall, P., and Tang, C.Y. (2018). A frequency domain analysis of the error distribution from noisy high-frequency data. <em>Biometrika. 105,353-369.<\/em><\/li>\n<\/ul>\n<p><strong>2016<\/strong><\/p>\n<ul>\n<li>Chang, J.,Tang, C.Y. and Wu, Y. (2016). \u00a0Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. <em>Annals of Statistics.44,515-539.<\/em><\/li>\n<\/ul>\n<p><strong>2015<\/strong><\/p>\n<ul>\n<li>Wu, T.T., Li,G. and Tang, C.Y. (2015). Empirical likelihood and variable selection for censored linear regression.<em> Scandinavian Journal of Statistics. 42,798-812.<\/em><\/li>\n<li>Zhang, W., Leng, C. and Tang, C.Y. (2015). A joint modeling approach for longitudinal studies. <em>Journal of the Royal Statistical Society, Series B. 77,219-238. <\/em><\/li>\n<\/ul>\n<p><strong>2014<\/strong><\/p>\n<ul>\n<li>Liu, C. and Tang, C.Y. (2014). A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data. <em> Journal of Econometrics. 180,217-232. <\/em><\/li>\n<li>Tang, C.Y. and Wu, T.T. (2014). Nested coordinate descent algorithms for empirical likelihood. <em> Journal of Statistical Computation and Simulation. 84,1917-1930. <\/em><\/li>\n<\/ul>\n<p><strong>2013<\/strong><\/p>\n<ul>\n<li>Chang, J., Tang, C.Y. and Wu, Y. (2013). Marginal empirical likelihood and sure independence screening. <em>Annals of Statistics. 41, 2132-2148. <\/em><\/li>\n<li>Liu, C. and Tang, C.Y. (2013). A state space model approach to integrated covariance matrix estimation with high frequency data. <em> Statistics and Its Interface.6, 463-475. <\/em><\/li>\n<li>Tang, C.Y. and Fan, Y. (2013). Discussion of &#8220;Large covariance estimation by thresholding principal orthogonal complements&#8221;. <em>Journal of the Royal Statistical Society, Series B. 75, 671. <\/em><\/li>\n<li>Fan, Y. and Tang, C.Y. (2013). Tuning parameter selection in high dimensional penalized likelihood. <em> Journal of the Royal Statistical Society, Series B. 75, 531-552.<\/em><\/li>\n<li>Chen, S.X., Qin, J. and Tang, C.Y. (2013). Mann-Whitney test with adjustments to pre-treatment variables for missing values and observational study. <em> Journal of the Royal Statistical Society, Series B. 75, 81-102. <\/em><\/li>\n<\/ul>\n<p><strong>2012<\/strong><\/p>\n<ul>\n<li>Tang, C.Y. and Qin, Y. (2012). An efficient empirical likelihood approach for estimating equations with missing data. <em> Biometrika. 99, 1001-1007. <\/em><\/li>\n<li>Leng, C. and Tang, C.Y. (2012). Sparse matrix graphical models. <em> Journal of the American Statistical Association. 107, 1187-1200.<\/em><\/li>\n<li>Leng, C. and Tang, C.Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. <em> Biometrika. 99, 703-716. <\/em><\/li>\n<li>Tang, C.Y. and Leng, C. (2012). An empirical likelihood approach to quantile regression with auxiliary information. <em>Statistics and Probability Letters. 82, 29-36.<\/em><\/li>\n<\/ul>\n<p><strong>2011<\/strong><\/p>\n<ul>\n<li>Tang, C.Y. and Leng, C. (2011). Empirical likelihood and quantile regression in longitudinal data analysis. <em>Biometrika. 98, 1001-1006.<\/em><\/li>\n<li>Chen, S.X. and Tang, C.Y. (2011). Nonparametric regression with discrete covariates and missing values. <em>Statistics and Its Interface. 4, 463-474.<\/em><\/li>\n<li>Chen, S.X. and Tang, C.Y. (2011). Properties of census dual system population size estimators. <em>International Statistical Review. 79, 336-361.<\/em><\/li>\n<li>Leng, C. and Tang, C.Y. (2011). Improving variance function estimation in semiparametric longitudinal data analysis. <em>The Canadian Journal of Statistics. 39, 656-670.<\/em><\/li>\n<\/ul>\n<p><strong>2010<\/strong><\/p>\n<ul>\n<li>Tang, C.Y. and Leng, C. (2010). Penalized high dimensional empirical likelihood. <em>Biometrika. 97, 905-920.<\/em><\/li>\n<li>Chen, S.X., Tang, C.Y. and Mule, V. T. (2010). Local post-stratification\u00a0 in dual system accuracy and coverage evaluation for the US Census.<em> Journal of the American Statistical Association, Applications and Case Studies. 105, 105-119<\/em>.<\/li>\n<\/ul>\n<p><strong>2009 and earlier<\/strong><\/p>\n<ul>\n<li>Tang, C.Y. and Chen, S.X. (2009). Parameter estimation and bias correction for diffusion processes.<i> Journal of Econometrics. 149, 65-81<\/i><em>.<\/em><\/li>\n<li>Chen, S.X., Gao, J. and Tang, C.Y. (2008). A test for model specification of diffusion processes.<em> Annals of Statistics. 36, 167-198.<\/em><\/li>\n<li>Chen, S.X. and Tang, C.Y. (2005). Nonparametric inference of value at risk for dependent financial returns. <em>Journal of Financial Econometrics<\/em>. <em>3, 227-255.<\/em><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Research Grants: PI, a Subaward of NIH R01GM140476 Co-PI, NSF DMS-2210687 PI, NSF IIS-1546087. PI, NSF SES-1533956. Research interests: methods, theory, and applications in statistics and data science: Empirical likelihood Longitudinal and dependent data analysis High-dimensional data analysis Financial statistics &hellip; <a href=\"https:\/\/sites.temple.edu\/yongtang\/research\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":5170,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-8","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/pages\/8","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/users\/5170"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/comments?post=8"}],"version-history":[{"count":21,"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/pages\/8\/revisions"}],"predecessor-version":[{"id":430,"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/pages\/8\/revisions\/430"}],"wp:attachment":[{"href":"https:\/\/sites.temple.edu\/yongtang\/wp-json\/wp\/v2\/media?parent=8"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}