The extended skew Gaussian process for regression

被引：4

作者：

Alodat M.T. ^{[1
]}

Al-Rawwash M.Y. ^{[2
]}

机构：

[1] Department of Mathematics, Statistics and Physics, Qatar University, Doha

[2] Department of Mathematics, University of Sharjah, Sharjah

来源：

METRON | 2014年 / 72卷 / 3期

关键词：

Extended skew normal distribution; Gaussian process for regression;

D O I：

10.1007/s40300-014-0046-z

中图分类号：

学科分类号：

摘要：

In this article, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression (ESGP) model. The ESGP model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGP model at a new input. Also we apply the ESGP model to FOREX data and we find that it fits the Forex data better than the GPR model. © 2014 Sapienza Università di Roma.

引用

页码：317 / 330

页数：13

共 50 条

[31] Sparse Spectrum Gaussian Process Regression
Lazaro-Gredilla, Miguel
Quinonero-Candela, Joaquin
Rasmussen, Carl Edward
Figueiras-Vidal, Anibal R.
JOURNAL OF MACHINE LEARNING RESEARCH, 2010, 11 : 1865 - 1881
[32] Hierarchical Gaussian process mixtures for regression
J.Q. Shi
R. Murray-Smith
D.M. Titterington
Statistics and Computing, 2005, 15 : 31 - 41
[33] Efficient sparsification for Gaussian process regression
Schreiter, Jens
Duy Nguyen-Tuong
Toussaint, Marc
NEUROCOMPUTING, 2016, 192 : 29 - 37
[34] Gaussian Process Regression on Nested Spaces
Blanchet-Scalliet, Christophette
Demory, Bruno
Gonon, Thierry
Helbert, Celine
SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2023, 11 (02): : 426 - 451
[35] Variational Tobit Gaussian Process Regression
Basson, Marno
Louw, Tobias M. M.
Smith, Theresa R. R.
STATISTICS AND COMPUTING, 2023, 33 (03)
[36] Dynamic Transfer Gaussian Process Regression
Wei, Pengfei
Qu, Xinghua
Song, Wen
Ma, Zejun
PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022, 2022, : 2118 - 2127
[37] Implicit Manifold Gaussian Process Regression
Fichera, Bernardo
Borovitskiy, Viacheslav
Krause, Andreas
Billard, Aude
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
[38] Compressed Gaussian Process for Manifold Regression
Guhaniyogi, Rajarshi
Dunson, David B.
JOURNAL OF MACHINE LEARNING RESEARCH, 2016, 17
[39] Coded Distributed Gaussian Process Regression
Zeulin, Nikita
Galinina, Olga
Himayat, Nageen
Andreev, Sergey
IEEE COMMUNICATIONS LETTERS, 2023, 27 (01) : 372 - 376
[40] GAUSSIAN PROCESS REGRESSION IN THE FLAT LIMIT
Barthelme, Simon
Amblard, Pierre-Oliviera
Remblay, Nicolas
Usevich, Konstantin
ANNALS OF STATISTICS, 2023, 51 (06): : 2471 - 2505

← 1 2 3 4 5 →