{"id":2124,"date":"2024-12-06T11:00:52","date_gmt":"2024-12-06T03:00:52","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=2124"},"modified":"2025-07-09T16:27:39","modified_gmt":"2025-07-09T08:27:39","slug":"representative-work-series-one-a-series-of-new-methods-for-high-dimensional-covariate-screening-and-data-dimensionality-reduction","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/en\/publications-en\/publications-proxy-en\/2124\/","title":{"rendered":"Representative Work Series 1: A Series of New Methods for High-Dimensional Covariate Screening and Data Dimensionality Reduction"},"content":{"rendered":"<ul>\n<li><span style=\"color: #000000;\"><a style=\"color: #000000;\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2025\/07\/On-the-Modeling-and-Prediction-of-High-Dimensional-Functional-Time-Series.pdf\"><strong>Chang, J.<\/strong>, Fang, Q., Qiao, X., &amp; Yao, Q. (2024+). On the modeling and prediction of high-dimensional functional time series. <strong><em>Journal of the American Statistical Association<\/em><\/strong>, in press.<\/a><\/span><\/li>\n<li><span style=\"color: #000000;\"><a style=\"color: #000000;\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/Modelling-matrix-time-series-via-a-tensor-CP-decomposition.pdf\"><strong>Chang, J.<\/strong>, <strong>He, J.<\/strong>, <strong>Yang, L.<\/strong>, &amp; Yao, Q. (2023). Modelling matrix time series via a tensor CP-decomposition. <em><strong>Journal of the Royal Statistical Society Series B<\/strong><\/em>, 85, 127-148.<\/a><\/span><\/li>\n<li><span style=\"color: #000000;\"><a style=\"color: #000000;\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/Principal-component-analysis-for-second-order-stationary-vector-time-series.pdf\"><strong>Chang, J.<\/strong>, Guo, B., &amp; Yao, Q. (2018). Principal component analysis for second-order stationary vector time series. <em><strong>The Annals of Statistics<\/strong><\/em>, 46, 2094-2124.<\/a><\/span><\/li>\n<li><span style=\"color: #000000;\"><a style=\"color: #000000;\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/Local-independence-feature-screening-for-nonparametric-and-semiparametric-models-by-marginal-empirical-likelihood.pdf\"><strong>Chang, J.<\/strong>, Tang, C. Y., &amp; Wu, Y. (2016). Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. <em><strong>The Annals of Statistics<\/strong><\/em>,\u00a0 44, 515-539.<\/a><\/span><\/li>\n<li><span style=\"color: #000000;\"><a style=\"color: #000000;\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/High-dimensional-stochastic-regression-with-latent-factors-endogeneity-and-nonlinearity.pdf\"><strong>Chang, J.<\/strong>, Guo, B., &amp; Yao, Q. (2015). High dimensional stochastic regression with latent factors, endogeneity and nonlinearity. <em><strong>Journal of Econometrics<\/strong><\/em>, 189,\u00a0 297-312.<\/a><\/span><\/li>\n<li><span style=\"color: #000000;\"><a style=\"color: #000000;\" href=\"https:\/\/changjinyuan.com\/wp-content\/uploads\/2024\/12\/Marginal-empirical-likelihood-and-sure-independence-feature-screening.pdf\"><strong>Chang, J.<\/strong>, Tang, C. Y., &amp; Wu, Y. (2013). Marginal empirical likelihood and sure independence feature screening, <em><strong>The Annals of Statistics<\/strong><\/em>, 41, 2123-2148.<\/a><\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>This type of research focuses on the model construction, prediction, and statistical analysis of high-dimensional time series data, mainly solving the problems of feature extraction and structured processing in high-dimensional complex dynamic data.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[19],"tags":[],"class_list":["post-2124","post","type-post","status-publish","format-standard","hentry","category-publications-proxy-en"],"acf":[],"lang":"en","translations":{"en":2124,"cn":2118},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2124","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/comments?post=2124"}],"version-history":[{"count":17,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2124\/revisions"}],"predecessor-version":[{"id":5105,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/2124\/revisions\/5105"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=2124"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=2124"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=2124"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}