{"id":6275,"date":"2026-09-25T17:30:26","date_gmt":"2026-09-25T09:30:26","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=6275"},"modified":"2026-09-29T16:32:57","modified_gmt":"2026-09-29T08:32:57","slug":"chang-and-huangs-paper-accepted-by-jrssb","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/en\/latest-news-en\/6275\/","title":{"rendered":"Chang and Huang’s Paper Accepted by JRSSB"},"content":{"rendered":"

The paper \u201cCP-factorization for High-Dimensional Tensor Time Series and Double Projection Iterations\u201d, co-authored by Prof. Jinyuan Chang and Associate Research Fellow Guanglin Huang from our team, together with Prof. Qiwei Yao of the London School of Economics and Political Science and Associate Prof. Long Yu of Shanghai University of Finance and Economics, has been officially accepted by the Journal of the Royal Statistical Society Series B.<\/em><\/p>\n

Abstract<\/strong><\/span><\/p>\n

We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed based on the serial dependence structure of the data. The asymptotic properties of the proposed estimator are established under a general setting as long as the factor loading vectors are linearly independent, allowing the factors to be correlated and the factor loading vectors to be not nearly orthogonal. The procedure adapts to the sparsity of the factor loading vectors, accommodates weak factors, and demonstrates strong performance across a wide range of scenarios. To further reduce estimation errors, we also introduce an iterative algorithm based on a novel double projection approach. We theoretically justify the improved convergence rate of the iterative estimator, and derive the associated limiting distribution. A consistent estimator of the asymptotic variance is also provided, which plays a key role in the related inference problems. All results are validated through extensive simulations and two real data applications.<\/p>\n

Author Introduction<\/strong><\/span><\/p>\n

Jinyuan Chang is a Guanghua Chair Professor at Southwestern University of Finance and Economics, Executive Dean of the Institute for Interdisciplinary Innovation in Statistics, and Executive Director of the Joint Laboratory of Data Science and Business Intelligence. His main research interests include complex data analysis.<\/p>\n

Guanglin Huang is an Associate Research Fellow at the Institute of Statistical\u00a0 Interdisciplinary Research, Southwestern University of Finance and Economics. His main research interests include time series analysis, financial risk management, and high-dimensional panel data analysis.<\/p>\n

Qiwei Yao is a Chair Professor at the London School of Economics and Political Science. His main research interests include time series analysis, dimension reduction and factor modelling, dynamic network modelling, spatio-temporal modelling, financial econometrics, and nonparametric regression.<\/p>\n

Long Yu is an Associate Professor at Shanghai University of Finance and Economics. His main research interests include factor models, high-dimensional data analysis, and random matrix theory.<\/p>\n","protected":false},"excerpt":{"rendered":"

The paper \u201cCP-factorization for High-Dimensional Tensor Time Series and Double Projection Iterations\u201d, co-authored by Prof. Jinyuan Chang and Associate Research Fellow Guanglin Huang from our team, together with Prof. Qiwei Yao of the London School of Economics and Political Science and Associate Prof. Long Yu of Shanghai University of Finance and Economics, has been officially accepted by the Journal of the Royal Statistical Society Series B.<\/p>\n","protected":false},"author":1,"featured_media":6282,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-6275","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-latest-news-en"],"acf":[],"lang":"en","translations":{"en":6275},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/6275","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=6275"}],"version-history":[{"count":2,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/6275\/revisions"}],"predecessor-version":[{"id":6281,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/6275\/revisions\/6281"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media\/6282"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=6275"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=6275"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=6275"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}