A robust estimation for the extended t-process regression model

被引：1

作者：

Wang, Zhanfeng ^{[1
]}

Li, Kai ^{[1
]}

Shi, Jian Qing ^{[2
]}

机构：

[1] Univ Sci & Technol China, Dept Stat & Finance, Sch Management, Hefei, Anhui, Peoples R China

[2] Newcastle Univ, Sch Math & Stat, Newcastle Upon Tyne, Tyne & Wear, England

来源：

STATISTICS & PROBABILITY LETTERS | 2020年 / 157卷

关键词：

Functional data; Maximum a posterior; Spike and slab priors; Information consistency; VARIABLE SELECTION; SPIKE;

D O I：

10.1016/j.spl.2019.108626

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Robust estimation and variable selection procedure are developed for the extended t-process regression model with functional data. Statistical properties such as consistency of estimators and predictions are obtained. Numerical studies show that the proposed method performs well. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：5

共 50 条

[41] Robust parameters estimation of regression model based on singular value decomposition
Li, SL
Yang, J
[J]. ISTM/2003: 5TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, CONFERENCE PROCEEDINGS, 2003, : 4612 - 4616
[42] Robust parameter estimation of regression model with AR(p) error terms
Tuac, Y.
Guney, Y.
Senoglu, B.
Arslan, O.
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2018, 47 (08) : 2343 - 2359
[43] Comparing Partial Likelihood and Robust Estimation Methods for the Cox Regression Model
Desmarais, Bruce A.
Harden, Jeffrey J.
[J]. POLITICAL ANALYSIS, 2012, 20 (01) : 113 - 135
[44] DIAGNOSTICS AND ROBUST ESTIMATION WHEN TRANSFORMING THE REGRESSION-MODEL AND THE RESPONSE
CARROLL, RJ
RUPPERT, D
[J]. TECHNOMETRICS, 1987, 29 (03) : 287 - 299
[45] Robust estimation in joint mean–covariance regression model for longitudinal data
Xueying Zheng
Wing Kam Fung
Zhongyi Zhu
[J]. Annals of the Institute of Statistical Mathematics, 2013, 65 : 617 - 638
[46] A Robust Estimation Method for Nonlinear Model Coefficients Using Ridge Regression
Xu, Qiang
Zhang, Wei
Wang, Guizhen
Xia, Xiangjie
Liu, Ying
Tang, Youxi
[J]. 2020 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2020), 2020,
[47] Robust Minimum Divergence Estimation for the Multinomial Circular Logistic Regression Model
Castilla, Elena
Ghosh, Abhik
[J]. ENTROPY, 2023, 25 (10)
[48] On Robust Estimation of Error Variance in (Highly) Robust Regression
Kalina, Jan
Tichavsky, Jan
[J]. MEASUREMENT SCIENCE REVIEW, 2020, 20 (01): : 6 - 14
[49] Robust two dimensional spectral estimation based on AR model excited by a t-distribution process
Sanubari, J
Tokuda, K
Onoda, M
[J]. 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, CONFERENCE PROCEEDINGS, VOLS 1-6, 1996, : 2998 - 3001
[50] Can Gaussian process regression be made robust against model mismatch?
Sollich, P
[J]. DETERMINISTIC AND STATISTICAL METHODS IN MACHINE LEARNING, 2005, 3635 : 199 - 210

← 1 2 3 4 5 →