Estimation and testing of expectile regression with efficient subsampling for massive data

Subsampling strategy plays a crucial role in statistical inference for massive data owing to its computing and storage superiority. The parameter estimation and hypothesis testing of expectile regression for massive data is of concern. This paper offers an alternative to the traditional asymmetric l...

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Vydáno v:	Statistical papers (Berlin, Germany) Ročník 65; číslo 9; s. 5593 - 5613
Hlavní autoři:	Chen, Baolin, Song, Shanshan, Zhou, Yong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024 Springer Nature B.V
Témata:	Algorithms Approximation Asymptotic methods Datasets Economic Theory/Quantitative Economics/Mathematical Methods Economics Finance Hypotheses Hypothesis testing Insurance Management Mathematics and Statistics Normality Operations Research/Decision Theory Parameter estimation Probability Theory and Stochastic Processes Random variables Regression analysis Regular Article Statistical analysis Statistical inference Statistical methods Statistical tests Statistics Statistics for Business Asymmetric least square (ALS) Estimator Kernel smoothing Subsampling strategy Newton’s iteration
ISSN:	0932-5026, 1613-9798
On-line přístup:	Získat plný text
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