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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 least square (ALS) estimator via smooth approximation of loss function. Then, an efficient subsampling algorithm based on Newton’s iteration is proposed. We prove consistency and asymptotic normality and provide the optimal subsampling probability and the proper order of smoothing parameter. We also apply the subsampling strategy for hypothesis testing, where the proposed test statistics have bigger power, compared with the test statistic based on the simple random subsampling. Simulation and two real data examples demonstrate the effectiveness of the proposed subsampling estimation and testing methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-024-01571-z