Wang
Qihua
Let fY|X,Z(y|x,z)\documentclass[12pt]{minimal}
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\begin{document}$$f_{Y|X,Z}(y|x,z)$$\end{document} be the conditional probability function of Y given (X, Z), where Y is the scalar response variable, while (X, Z) is the covariable vector. This paper proposes a robust model selection criterion for fY|X,Z(y|x,z)\documentclass[12pt]{minimal}
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\begin{document}$$f_{Y|X,Z}(y|x,z)$$\end{document} with X missing at random. The proposed method is developed based on a set of assumed models for the selection probability function. However, the consistency of model selection by our proposal does not require these models to be correctly specified, while it only requires that the selection probability function is a function of these assumed selective probability functions. Under some conditions, it is proved that the model selection by the proposed method is consistent and the estimator for population parameter vector is consistent and asymptotically normal. A Monte Carlo study was conducted to evaluate the finite-sample performance of our proposal. A real data analysis was used to illustrate the practical application of our proposal.
Robust model selection with covariables missing at random
probability function
estimator
analysis
conditions
https://doi.org/10.1007/s10463-021-00806-2
article
covariables
practical applications
study
Carlo study
paper
2021-08-25
conditional probability function
articles
robust model selection criteria
criteria
assumed model
data analysis
selection probability function
selection
variables
model
539-557
model selection criteria
2022-05-20T07:38
robust model selection
consistency
en
real data analysis
vector
selection criteria
finite sample performance
https://scigraph.springernature.com/explorer/license/
proposal
false
set
scalar response variable
Monte Carlo study
applications
function
response variables
method
2021-08-25
model selection
parameter vector
performance
doi
10.1007/s10463-021-00806-2
Zhongqi
Liang
Yuting
Wei
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China
School of Statistics and Mathematics, Zhejiang Gongshang University, 310018, Hangzhou, Zhejiang, China
Statistics
Mathematical Sciences
Department of Statistics and Finance, Universtiy of Science and Technology of China, 230026, Hefei, China
Department of Statistics and Finance, Universtiy of Science and Technology of China, 230026, Hefei, China
3
Springer Nature - SN SciGraph project
Annals of the Institute of Statistical Mathematics
0020-3157
1572-9052
Springer Nature
74
dimensions_id
pub.1140638256
School of Statistics and Mathematics, Zhejiang Gongshang University, 310018, Hangzhou, Zhejiang, China
School of Statistics and Mathematics, Zhejiang Gongshang University, 310018, Hangzhou, Zhejiang, China