Penalized empirical likelihood for longitudinal expectile regression with growing dimensional data

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY Journal of the Korean Statistical Society Pub Date : 2024-04-09 DOI:10.1007/s42952-024-00265-4
Ting Zhang, Yanan Wang, Lei Wang
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引用次数: 0

Abstract

Expectile regression (ER) naturally extends the classical least squares to investigate heterogeneous effects of covariates on the distribution of the response variable. In this paper, we propose a penalized empirical likelihood (PEL) based ER estimator, which incorporates quadratic inference function and generalized estimating equation to construct the PEL procedure for longitudinal data. We investigate the asymptotic properties of the PEL estimator when the number of covariates is allowed to diverge as the sample size increases. The finite-sample performance of the proposed estimator is studied through simulations, and an application to yeast cell-cycle gene expression data is also presented.

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纵向期望值回归的惩罚性经验似然法与维度不断增加的数据
期望回归(ER)自然地扩展了经典最小二乘法,以研究协变量对响应变量分布的异质性影响。本文提出了一种基于惩罚性经验似然法(PEL)的期望回归估计器,它结合了二次推断函数和广义估计方程来构建纵向数据的 PEL 程序。我们研究了当协变因素数量随样本量增加而发散时 PEL 估计器的渐近特性。我们通过模拟研究了所提出的估计器的有限样本性能,并介绍了它在酵母细胞周期基因表达数据中的应用。
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来源期刊
Journal of the Korean Statistical Society
Journal of the Korean Statistical Society 数学-统计学与概率论
CiteScore
1.30
自引率
0.00%
发文量
37
审稿时长
3 months
期刊介绍: The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.
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