{"id":365,"date":"2020-07-09T16:56:57","date_gmt":"2020-07-09T16:56:57","guid":{"rendered":"http:\/\/sites.rutgers.edu\/rong-chen\/?page_id=365"},"modified":"2026-07-30T01:37:55","modified_gmt":"2026-07-30T01:37:55","slug":"research","status":"publish","type":"page","link":"https:\/\/sites.rutgers.edu\/rong-chen\/research\/","title":{"rendered":"Research"},"content":{"rendered":"<h2>Interests<\/h2>\n<ul>\n<li>Analysis of time series of everything<\/li>\n<li>Monte Carlo Methods, Statistical Computing and Bayesian Analysis<\/li>\n<li>Statistical Applicatons in Science, Engineering and Business.<\/li>\n<\/ul>\n<h3><\/h3>\n<h3>Book<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.amazon.com\/Nonlinear-Analysis-Wiley-Probability-Statistics-ebook\/dp\/B07HDGRKYK\">Nonlinear Time Series Analysis (with Ruey Tsay)\u00a0<\/a>and\u00a0<a href=\"http:\/\/faculty.chicagobooth.edu\/ruey.tsay\/teaching\/nts\/\">its accompanying site for additional material<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2>Papers<\/h2>\n<ul>\n<li>[1] Chen, R. and Tsay, R.S. (1991)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/91AAP_ergodicity.pdf\">`On the ergodicity of TAR(1) processes&#8217;,\u00a0<\/a>The Annals of Applied Probability, 1, 613-634.<\/li>\n<li>[2] Chen, R. and Tsay, R.S. (1993)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/93JASA_FAR.pdf\">`Functional coe\u00b1cient autoregressive models&#8217;,\u00a0<\/a>Journal of American Statistical Association, 88, 298-308.<\/li>\n<li>[3] Chen, R. and Tsay, R.S. (1993)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/93JASA_NAAR.pdf\">`Nonlinear additive ARX models&#8217;,\u00a0<\/a>Journal of American Statistical Association, 88, 955-967.<\/li>\n<li>[4] Liu, J.S. and Chen, R. (1995)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/95JASA_Blind-Deconvolution.pdf\">`Blind deconvolution via sequential imputation&#8217;,\u00a0<\/a>Journal of American Statistical Association, 90, 567-576.<\/li>\n<li>[5] Chen, R., Liu, J.S. and Tsay, R.S. (1995)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/95IEEE-SP_blind-MCMC.pdf\">`Additivity tests for nonlinear autoregressive models&#8217;,\u00a0<\/a>Biometrika, 82, 369-383.<\/li>\n<li>[6] Chen, R. (1995) `Threshold variable selection of open-loop threshold AR models&#8217;, Journal of Time Series Analysis, 16, 461-481<\/li>\n<li>[7] Chen, R. and Li, T. (1995)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/95IEEE-SP_blind-MCMC.pdf\">`Blind restoration of linearly degraded discrete signals by Gibbs sampler&#8217;,\u00a0<\/a>IEEE Transactions on Signal Processing, 43, 2410-2413<\/li>\n<li>[8] Chen, R. (1996)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/96SS_prediction.pdf\">`A nonparametric multi-step prediction estimator in Markovian structures&#8217;,\u00a0<\/a>Statistica Sinica, 6, 603-615<\/li>\n<li>[9] Chen, R. and Liu, J.S. (1996)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/96JRSSB_predictive-updating.pdf\">`Predictive updating methods with applications to Bayesian classiffication&#8217;,\u00a0<\/a>Journal of the Royal Statistical Society, Series B, 58, 397-415<\/li>\n<li>[10] Chen, R. and Tsay, R.S. (1996) `Nonlinear transfer functions&#8217;, Journal of Nonparametric Statistics, 6, 193-204.<\/li>\n<li>[11] Cheng, Q., Chen, R. and Li, T. (1996)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/96IEEE-Geo_deconvolution_Gibbs.pdf\">`Simultaneous wavelet estimation and deconvolution of reflection Seismic signals via Gibbs sampler&#8217;,\u00a0<\/a>IEEE Transactions on Geoscience and Remote Sensing, 34, 377-384<\/li>\n<li>[12] Chen, R. (1996)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/96JMA_extra-information.pdf\">`Incorporating extra information in nonparametric smoothing&#8217;,\u00a0<\/a>Journal of Multivariate Analysis, 58, 133-150<\/li>\n<li>[13] Carroll, R.J., Chen, R., Li, T-H, Newton, H.J., Schmiediche, H. and Wang, N. (1997)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/97JASA_ozone.pdf\">`Trends in ozone exposure in Harris County, Texas&#8217;,\u00a0<\/a>Journal of American Statistical Association, (discussion paper), 92, 392-415<\/li>\n<li>[14] H\u00c4ardle, W., Chen, R. and Luetkepohl, H. (1997).\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/97ISR_review.pdf\">`A review of nonparametric time series analysis&#8217;,\u00a0<\/a>International Statistical Review, 65, 49-72<\/li>\n<li>[15] Linton, O. Chen, R. Wang, N. and H\u00c4ardle, W. (1997).\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/97JASA_transformation.pdf\">`An analysis of transformation for additive nonparametric regression&#8217;,\u00a0<\/a>Journal of American Statistical Association, 92, 1512-1521<\/li>\n<li>[16] Liu, J.S. and Chen, R. (1998)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/98JASA_SMC.pdf\">`Sequential Monte Carlo methods for dynamic systems&#8217;,\u00a0<\/a>Journal of American Statistical Association, 93, 1032-1043<\/li>\n<li>[17] Liu, J.S. Chen, R. and Wong, W.H. (1998)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/98JASA_rejection-control.pdf\">`Rejection control and importance sampling&#8217;,\u00a0<\/a>Journal of American Statistical Association, 93, 1022-1031<\/li>\n<li>[18] Chen, R. and Fomby, T. (1999)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/99JBES_Hawaii.pdf\">`Forecasting with stable seasonal pattern models with an application of Hawaiian tourist data&#8217;,\u00a0<\/a>Journal of Business &amp; Economic Statistics, 17, 497-504<\/li>\n<li>[19] Speed, F.M., Smith, W.B., Chen, R., and Speed, F.M. Jr (1999), `<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/99Communication_tidal.pdf\">Analysis of tidal data and datums: Accessible examples of harmonic modeling with autocorrelation and imputation&#8217;,<\/a> Communications in Statistics, Part A &#8211; Theory and Methods, 28, 2947-2965<\/li>\n<li>[20] Chen, R. and Liu, J.S. (2000)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/00JRSSB_MKF.pdf\">`Mixture Kalman Filters&#8217;,\u00a0<\/a>Journal of the Royal Statistical Society, Series B, 62, 493-508<\/li>\n<li>[21] Chen, R., Wang, X. and Liu, J.S. (2000)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/00IEEE-IT_flat_fading.pdf\">&#8216;Adaptive Joint Detection and Decoding in Flat-Fading Channels via Mixture Kalman Filtering&#8217;.\u00a0<\/a>IEEE trans. information theory, 46, 2079-2094<\/li>\n<li>[22] Wang, X. and R. Chen. (2000)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/00IEEE-SP_multi-user.pdf\">&#8216;Adaptive MAP multiuser detection for synchronous CDMA with Gaussian and non-Gaussian noise&#8217;,\u00a0<\/a>IEEE trans. signal processing, 48, 2013-2028<\/li>\n<li>[23] Chen, R. and Liu, L. (2001)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/01JTSA_FAR2.pdf\">&#8216;Functional coeffi<\/a><a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/01JTSA_FAR2.pdf\">cient autoregressive models: estimation and tests of hypotheses&#8217;, <\/a>J. Time Series Analysis, 22, 151-174<\/li>\n<li>[24] Wang, X. and R. Chen. (2001)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/01IEEE-VehicularTech_BlindTurbo.pdf\">&#8216;Blind Turbo equalization in Gaussian and impulsive noises&#8217;.\u00a0<\/a>IEEE trans. Vehicular Technology, 50, 1092-1105<\/li>\n<li>[25] Liu, L-M, Bhattacharyya, S., Sclove, S.L., Chen, R. and Lattyak, W.J. (2001)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/01CSDA_timeseries-datamining.pdf\">&#8216;Data mining on time series: an illustration using fast-food restaurant franchise data&#8217;,\u00a0<\/a>Computational Statistics and Data Analysis, 37, 455-476<\/li>\n<li>[26] Wang, X., Chen, R. and Liu, J.S. (2002)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/02IEEE-VLSI_signal-processing.pdf\">&#8216;Monte Carlo Bayesian signal processing for wireless communication&#8217;,\u00a0<\/a>IEEE trans. VLSI Signal Process, 30, 89-105<\/li>\n<li>[27] Chen, R., Liu, J.S., and Wang, X. (2002)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/02IEEE-SP_convergence.pdf\">&#8216;Convergence Properties of the Gibbs Sampler in Some Digital Communications Problems&#8217;,\u00a0<\/a>IEEE trans. Signal Process, 50, 255-270<\/li>\n<li>[28] Wang, X., Chen, R. and Guo, D. (2002)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/02IEEE-SP_delay-pilot.pdf\">&#8216;Delayed Pilot Sampling for Mixture Kalman Filter with Application in Fading Channels&#8217;,\u00a0<\/a>IEEE trans. Signal Process, 50, 241-254<\/li>\n<li>[29] Chen, R. and J.S. Liu. (2002)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/02Test_discussion.pdf\">Discussion of &#8216; Spatial-Temporal Nonlinear Filtering Based on Hierarchical Statistical Models&#8217; by M. E. Irwin, N. Cressie, and G. Johannesson.\u00a0<\/a>Test, 11, 282-284.<\/li>\n<li>[30] Liang, J, Zhang, J. and Chen, R. (2002)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/02JCP_packing-geometry.pdf\">&#8216;Statistical geometry of packing defects of lattice chain polymer for enumeration and sequential Monte Carlo method&#8217;,\u00a0<\/a>Journal of Chemical Physics, 117, 3511-3521<\/li>\n<li>[31] Guo, D. Wang, X. and Chen, R. (2003)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/03AIMS_adaptive_fading.pdf\">&#8216;Nonparametric Adaptive Detection in Fading Channels Based on Sequential Monte Carlo and Bayesian Model Averaging&#8217;,\u00a0<\/a>Annuals of Institute of Statistical Mathematics, 55, 423-436.<\/li>\n<li>[32] Zhang, J., R. Chen, C. Tang and J. Liang (2003)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/03JCP_packing-density.pdf\">&#8216;Origin of scaling behavior of protein packing density: A sequential Monte Carlo study of compact long chain polymer&#8217;,\u00a0<\/a>Journal of Chemical Physics, 118, 6102-6109<\/li>\n<li>[33] Chen, R., Yang, L. and Hafner, C. (2004)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/04JRSSB_prediction.pdf\">&#8216;Nonparametric multi-step prediction in time series&#8217;,\u00a0<\/a>Journal of the Royal Statistical Society, Series B. 66, 669-686.<\/li>\n<li>[34] Guo, D., Wang, X., and Chen, R.(2004)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/04IEEE-SP_waveletSMC.pdf\">`Wavelet-based Sequential Monte Carlo Blind Receivers in Fading Channels with Unknown Channel Statistics&#8217;,\u00a0<\/a>IEEE Transactions on Signal Processing. 52, 227-239<\/li>\n<li>[35] Zhang, J., Chen, Y., Chen, R., and Liang, J. (2004)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/04JCP_charility.pdf\">`Importance of chirality and reduced flexibility of protein side chains: A study with square and tetrahedral lattice models&#8217;,\u00a0<\/a>Journal of Chemical Physics, 121, 592-603<\/li>\n<li>[36] Hjellvik, V., Chen, R., and Tjostheim, D. (2004)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/04JTSA_nonparametric-panel.pdf\">&#8216;Nonparametric estimation and testing in panels of intercorrelated time series&#8217;,\u00a0<\/a>Journal of Time Series Analysis 25, 831-872<\/li>\n<li>[37] Guo, D., Wang, X. and Chen, R. (2004)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/04EURASIP_Multi-level_MKF.pdf\">&#8216;Multilevel Mixture Kalman Filter&#8217;,\u00a0<\/a>EURASIP Journal on Applied Signal Processing, Special issue on Particle Filtering, 15, 2255-2266.<\/li>\n<li>[38] Guo, D., Wang, X. and Chen, R. (2005)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/05StatComp_new-SMC.pdf\">&#8216;New Sequential Monte Carlo Methods for Nonlinear Dynamic Systems&#8217;,\u00a0<\/a>Statistics and Computing. 15, 135-147<\/li>\n<li>[39] Lin, M., Zhang, J., Cheng, Q. and Chen, R. (2005)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/05JASA_IPF.pdf\">`Independent particle filters&#8217;,\u00a0<\/a>Journal of American Statistical Association, 100, 1412-1421.<\/li>\n<li>[40] Liu, J.M, Liu, L-M and Chen, R. (2006)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/06JoF_Electricity-Loading.pdf\">`Modeling hourly electricity loads using a semiparametric time series approach&#8217;,\u00a0<\/a>Journal of Forecasting, 25, 537-559.<\/li>\n<li>[41] Zhang, J., Chen, R. and Liang, J. (2006)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/06PROTEINS_Potential-Function.pdf\">&#8216;Empirical potential function for simpliffied protein models: combining contact and local sequence structure descriptors&#8217;,\u00a0<\/a>PROTEINS: Structure, Function, and Bioinformatics, 63, 949-960.<\/li>\n<li>[42] Zhang, J., Lin, M., Chen, R., Liang, J. and Jun S. Liu (2007)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/07PROTEINS_Near_Native.pdf\">Monte Carlo sampling of Near-Native structures of proteins with applications&#8217;,\u00a0<\/a>PROTEINS: Structure, Function, and Bioinformatics, 66, 61-68.<\/li>\n<li>[43] Wu, S, and Chen, R. (2007)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/07SS_threshold.pdf\">`Threshold variable selection and threshold variable driven switching autoregressive models&#8217;,\u00a0<\/a>Statistica Sinica, 17, 241-264.<\/li>\n<li>[44] Zhang, J.L., Lin, M, Liu, J.S. and Chen. R. (2007)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/07SS_variable-selection.pdf\">`Lookahead and piloting strategies for variable selection&#8217;,\u00a0<\/a>Statistica Sinica, 17, 985-1005.<\/li>\n<li>[45] Chen, C.T., Chen, R. and Bassett, G.W. (2007)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/07JBF_index_mine.pdf\">&#8216;Fundamental Indexation via Smoothed Cap Weights&#8217;,\u00a0<\/a>J. Finance and Banking, 31, 3486-3502.<\/li>\n<li>[46] Lin, M., Chen, R. and Liang, J. (2008)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/08JCP_void.pdf\">&#8216;Statistical geometry of lattice chain polymers with voids of defined shapes: Sampling with strong constraints&#8217;,\u00a0<\/a>J. Chemical Physics, 128(084903), 1-12<\/li>\n<li>[47] Zhang, J., Lin, M., Chen, R., Wang, W. and Liang, J. (2008)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/08JCP_RNAloopEntropy.pdf\">&#8216;Discrete State Model and Accurate Estimation of Loop Entropy of RNA Secondary Structures&#8217;,\u00a0<\/a>J. Chemical Physics, 128(125107), 1-10<\/li>\n<li>[48] Lin, M., Lu, H., Chen, R. and Liang, J. (2008)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/08JCP_phi-value.pdf\">&#8216;Generating properly weighted ensemble of conformations of proteins from sparse or indirect distance constraints&#8217;,\u00a0<\/a>J. Chemical Physics, 129(094101), 1-13.<\/li>\n<li>[49] Feng, X., Chen, R. and Bassett, G.W. (2008)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/08SII_Q_MoM.pdf\">&#8216;Quantile Momentum&#8217;,\u00a0<\/a>Statistics and Its Interface, 1, 243-254.<\/li>\n<li>[50] Cai, A.M., Tsay, R.S. and Chen, R. (2009)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/09JCGS_Variable_Selection.pdf\">`Variable selection in linear regression with many predictors&#8217;,\u00a0<\/a>J. Computational and Graphical Statistics, 18, 573-591<\/li>\n<li>[51] Zhang, J., Dundas, J., Lin, M., Chen, R., Wang, W. and Liang, J. (2009)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/09RNA_Pseudoknotted_RNA.pdf\">`Prediction of geometrically feasible three dimensional structures of Pseudoknotted RNA through free energy&#8217;,\u00a0<\/a>RNA, 15, 2248-2263.<\/li>\n<li>[52] Liu, J.M., Chen, R. and Yao, Q. (2010)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/10JEconometrics_Nonparametric_Transfer.pdf\">`Nonparametric transfer function models&#8217;,\u00a0<\/a>J. Econometrics, 157 151-164.<\/li>\n<li>[53] Lin, M., Chen, R. and Mykland, P. (2010)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/10JASA_diffusion_bridge.pdf\">&#8216;On generating Monte Carlo samples of continuous diffusion bridges&#8217;,\u00a0<\/a>Journal of American Statistical Association, 105, 820-838.<\/li>\n<li>[54] Chen, R., Lin, M. and Guo, R. (2010)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/10JASA_IPO.pdf\">`Self-Selection in Decision to Withdraw IPOs&#8217;,\u00a0<\/a>Journal of American Statistical Association, 105, 1297-1309.<\/li>\n<li>[55] Kang, Z., Zhang, L. and Chen, R. (2010)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/10SII_-KangZhangChen.pdf\">`Forecasting return volatility in the presence of microstructure noise&#8217;,\u00a0<\/a>Statistics and Its Interface, 3, 145-158.<\/li>\n<li>[56] Chen, R., Liang, H. and Wang, J. (2011)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/11J_Nonparametric_Additive_Model.pdf\">&#8216;Determination of linear components in additive models&#8217;,\u00a0<\/a>Journal of Nonparametric Statistics, 23, 367-383.<\/li>\n<li>[57] Lin, M., Zhang, J., Lu, H-M, Chen, R. and Liang J. (2011)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/11JCP_protein_folding.pdf\">&#8216;Constrained proper sampling of conformations of transition state ensemble during protein folding&#8217;.\u00a0<\/a>Journal of Chemical Physics, 134(075103) 1-13.<\/li>\n<li>[58] Chen, S., Chen, R., Ardell, G. and Lin, B. (2011)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/11JOT-trading_volume.pdf\">&#8216;End-of-day stock trading volume prediction with a two-component hierarchical model&#8217;,\u00a0<\/a>Journal of Trading, summer, 1-8.<\/li>\n<li>[59] Wang, J., Hua, L. and Chen, R. (2012)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/12JTSA_State_Space_HIV.pdf\">&#8216;A state-space model approach for modeling HIV infection dynamics&#8217;,\u00a0<\/a>Journal of Time Series Analysis, 33, 841-849.<\/li>\n<li>[60] Chen, S., Min, W. and Chen, R. (2013)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/13SS_TS_identification.pdf\">&#8216;Model identiffication for time series with dependent innovations&#8217;,\u00a0<\/a>Statistica Sinica, 23, 873-899<\/li>\n<li>[61] Lin, M., Chen, R. and Liu, J.S. (2013)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/13STS_delay.pdf\">&#8216;Lookahead strategies for sequential Monte Carlo&#8217;,\u00a0<\/a>Statistical Science, 28, 69-94<\/li>\n<li>[62] Cheng, J., Xie, M., Chen, R. and Roberts, F. (2013)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/13JASA_Surveillance.pdf\">&#8216;A latent source model to detect multiple spatial clusters with application in a mobile sensor network for surveillance of nuclear materials&#8217;,\u00a0<\/a>Journal of American Statistical Association, 108, 902-913.<\/li>\n<li>[63] Li, W., Tan, Z., and Chen, R. (2013)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/13JASA_ImportanceSampling.pdf\">&#8216;Two-stage important sampling with mixture proposals&#8217;,\u00a0<\/a>Journal of American Statistical Association}, 108, 1350-1360.<\/li>\n<li>[64] Liu, X., Cai, Z. and Chen, R. (2015),\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/15CS_Functional_Seasonal.pdf\">&#8216;Functional Coefficient Seasonal Time Series Model with an Application of Hawaii Tourism Data&#8217;,\u00a0<\/a>Computational Statistics, 33, 719-744.<\/li>\n<li>[65] Zheng, T., Xiao, H. and Chen, R. (2015)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/15JoE_GARMA.pdf\">&#8216;Generalized ARMA Models with Martingale Difference Errors&#8217;,\u00a0<\/a>Journal of Econometrics, 189, 492-506<\/li>\n<li>[66] Li, W., Tan, Z., and Chen, R. (2016)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/16JASA_SMC_MultipleProposal.pdf\">&#8216;Efficient Sequential Monte Carlo with Multiple Proposals and Control Variates&#8217;,\u00a0<\/a>Journal of American Statistical Association, 111, 298-313<\/li>\n<li>[67] Liu, X. and Chen, R. (2016)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/16SS_switching_factor.pdf\">&#8216;Regime-switching factor models for high-dimensional time series&#8217;,\u00a0<\/a>Statistica Sinica, 26, 1427-1451<\/li>\n<li>[68] Liu, X., Xiao, H. and Chen, R. (2016)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/16JoE_Functional.pdf\">&#8216;Convolutional Autoregressive Models for Functional Time Series&#8217;,\u00a0<\/a>Journal of Econometrics, 194, 263-282<\/li>\n<li>[69] Lin, M., Suess, E., Shumway, R. and Chen, R. (2016),\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/16JTSA_seismic.pdf\">&#8216;Bayesian deconvolution of signals observed on arrays&#8217;,\u00a0<\/a>J. Time Series Analysis, 37, 837-850.<\/li>\n<li>[70] Chang, K., Chen, R. and T. Fomby, T., (2017)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/17JF_compositional_seasonal.pdf\">&#8216;Prediction-based adaptive compositional model for seasonal time series analysis&#8217;,\u00a0<\/a>Journal of Forecasting, 36, 842-853<\/li>\n<li>[71] Zheng, T. and Chen, R. (2017)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/17JMVA_DARMA.pdf\">&#8216;Dirichlet ARMA models for compositional time series&#8217;,\u00a0<\/a>J. Multivariate Analysis, 158, 31-46.<\/li>\n<li>[72] Grelaud, A., Mitra, P., Chen, R. and Xie, M. (2018)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/18ASMBI_Nuclear.pdf\">&#8216;A dynamic system approach to real time nuclear source detection with mobile sensor networks&#8217;,\u00a0<\/a>Applied Stochastic Models in Business and Industry, 34, 4-19<\/li>\n<li>[73] Zhang, B. and Chen, R. (2018)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/18Classification_Clustering_TS.pdf\">&#8216;Nonlinear time series clustering based on Kolmogorov-Smirnov 2D statistic&#8217;,\u00a0<\/a>Journal of Classification, 35, 394-421<\/li>\n<li>[74] Zhao, Z., Zhang, Z. and Chen, R. (2018)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/18JoE_Maxima.pdf\">&#8216;Modeling Maxima with Autoregressive Conditional Fr\u00e9chet Model&#8217;.\u00a0<\/a>J. Econometrics, 207, 325-351.<\/li>\n<li>[75] Wang, D., Liu, X. and Chen, R. (2019)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/19JoE_MatrixFactor.pdf\">&#8216;Matrix factor models for high dimensional time series&#8217;,\u00a0<\/a>J. Econometrics, 208, 231-248<\/li>\n<li>[76] Wei, X., Zhang, P., Chen, R. and Zhou, Z. (2019)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/19IEEE_Power_Wind.pdf\">&#8216;A nonparametric Bayesian framework for short-term wind power probabilistic forecast&#8217;,\u00a0<\/a>IEEE Transactions on Power Systems, 34, 371-379<\/li>\n<li>[77] Chen, Y, Tsay, R.S. and Chen, R. (2020)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/20JASA_Constrained_Matrix.pdf\">&#8216;Constrained factor models for high-dimensional matrix-variate time series&#8217;,\u00a0<\/a>Journal of American Statistical Association, 115, 775-793.<\/li>\n<li>[78] Liu, X. and Chen, R. (2020)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/20JoE_Threshold_Factor.pdf\">&#8216;Threshold factor models for high-dimensional time series&#8217;,\u00a0<\/a>J. Econometrics, 216, 53-70<\/li>\n<li>[79] Cai, C, Chen, R, Xie, M (2020).\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/20WIREs_fusion.pdf\">Individualized inference through fusion learning, <\/a>WIREs Comput Stat. 12, 1-10<\/li>\n<li>[80] Liu, X, Chen, R. and Tsay, R.S. (2020)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/20RJ_NTSpackage.pdf\">&#8216;NTS: An R Package for Nonlinear Time Series Analysis&#8217;, <\/a>The R Journal, 2, 293-310.<\/li>\n<li>[81] Chen, R., Xiao, H, and Yang, D. (2021)\u00a0<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/21JoE_MatrixAR.pdf\">&#8216;Autoregressive models for matrix-valued time series&#8217;,\u00a0<\/a>J. Econometrics, 222, 539-560.<\/li>\n<li>[82] Chen, R., Yang, D. and Zhang, C.-H. (2021) <a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22JASA_tensorTS.pdf\">Factor model for high-dimensional tensor time series&#8217; (with discussion), <\/a>J, Journal of American Statistical Association, 117. 94-132.<\/li>\n<li>&#8212;&#8211; <a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22JASA_tensorTS_supplement.pdf\">supplement<\/a><\/li>\n<li>&#8212;&#8211; <a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22JASA_tensorTS_Rejoinder.pdf\">rejoinder<\/a><\/li>\n<li>[83] Zheng, T., Xiao, H. and Chen, R. (2022) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22JTSA_GARMA_GARCH.pdf\">Generalized autoregressive moving average models with GARCH errors<\/a>\u2019, Journal of Time Series Analysis, 43, 125-146<\/li>\n<li>[84] Han, Y., Chen, R. and Zhang, C.-H. (2022) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22EJS_rank_determination.pdf\">Rank determination in tensor factor model<\/a>\u2019, Electronic Journal of Statistics, 16, 1726-1803.<\/li>\n<li>[85] Cai, C., Chen, R. and Xiao, H. (2022) <a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22JMLR_KoPA.pdf\">\u2019KoPA: Automated Kronecker product approximation\u2019<\/a>, Journal of Machine Learning Research, 23, 1-44<\/li>\n<li>[86] Xiao, H., Chen, R. and Guerard, J. (2022) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22Wilmott-forecasting.pdf\">Forecasting the U.S. unemployment rate: another look<\/a>\u2019, Wilmott, 2022(122).<\/li>\n<li>[87] Cai, C., Chen, R. and Xiao, H. (2022) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/22JCGS_hybrid_KOPA.pdf\">Hybrid Kronecker product decomposition and approximation\u2019<\/a>, J. Computational and Graphical Statistics. 32, 838-852<\/li>\n<li>[88] Chen, Y. and Chen, R. (2023) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/23JDS_transport_network.pdf\">Modeling dynamic transport network with matrix factor models: an application to international trade flow<\/a>\u2019, J. Data Science, 21, 490-507.<\/li>\n<li>[89] Cai, C., Xie, M. and Chen, R. (2023) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/23JASA_Individualized-Group-Learning.pdf\">Individualized group learning<\/a>\u2019, Journal of American Statistical Association, 118, 622-638<\/li>\n<li>[90] Chen, R., Ji, Y., Jiang, G., Xie, R. and Zhu, P. (2023) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/23JBES_index.pdf\">Composite index construction with expert opinion<\/a>\u2019, Journal of Business &amp; Economic Statistics, 41, 67-79<\/li>\n<li>[91] Xiao, H., Han, Y. and Chen, R. (2023+) \u2019Reduced rank autoregressive models for matrix time series\u2019, Journal of Business &amp; Economic Statistics, in press.<\/li>\n<li>[92] Cai, C., Lin, M. and Chen, R. (2024) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/24Sinica_constrainedSMC_compressed.pdf\">Resampling strategy in Sequential Monte Carlo for constrained sampling problems<\/a>\u2019, Statistica Sinica, 34, 1178-1214<\/li>\n<li>[93] Han, Y., Chen, R., Zhang, C.-H. and Yao, Q. (2024) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/24JASA_decorrelation.pdf\">Simultaneous decorrelation of matrix time series<\/a>\u2019, Journal of American Statistical Association, 119, 957-969.<\/li>\n<li>[94] Han, Y., Yang, D., Zhang, C.-H. and Chen, R. (2024) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/24JBES_CPtensor.pdf\">CP factor model for dynamic tensors\u2019,<\/a> Journal of the Royal Statistical Society, series B, 86, 1383 &#8211; 1413<\/li>\n<li>[95] Dong, C., Chen, R., Xiao, Z. and Liu, W. (2024) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/24JoE_quantile.pdf\">Functional Quantile Autoregression<\/a>\u2019, J. Econometrics, 244, 105765<br \/>\n[96] Han, Y., Chen, R., Yang, D. and Zhang, C.-H. (2024) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/24AOS_IterativeProj.pdf\">Tensor factor model estimation by iterative projection\u2019<\/a>, Annal of Statistics, 52, 2641-2667.<\/li>\n<li>[97] Cai, C. and Chen, R. (2025) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/25SS_SMC_Anneal.pdf\">State space emulation and annealed Sequential Monte Carlo for High Dimensional Optimization<\/a>\u2019, Statistica Sinica, 35, 67-89<\/li>\n<li>[98] Bolivar, S, Huang, S-C, and Chen, R (2026) \u2019<a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/wp-content\/uploads\/sites\/1594\/2026\/07\/26AnnalsReview_tensorTS.pdf\">Analysis of Tensor Time Series<\/a>\u2019, Annual Review of Statistics and Its Application, 13, 369-398.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Interests Analysis of time series of everything Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applicatons in Science, Engineering and Business. Book Nonlinear Time Series Analysis (with Ruey Tsay)\u00a0and\u00a0its &hellip; <a href=\"https:\/\/sites.rutgers.edu\/rong-chen\/research\/\" class=\"\">Read More<\/a><\/p>\n","protected":false},"author":21,"featured_media":561,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-365","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Research - Rong Chen<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.rutgers.edu\/rong-chen\/research\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research - Rong Chen\" \/>\n<meta property=\"og:description\" content=\"Interests Analysis of time series of everything Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applicatons in Science, Engineering and Business. 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