Two-stage estimation and bias-corrected empirical likelihood in a partially linear single-index varying-coefficient model

IF 3.1 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-27 DOI:10.1093/jrsssb/qkad060

L. Xue

引用次数: 0

Abstract

The estimation and empirical likelihood (EL) of the parameters of interest in a partially linear single-index varying-coefficient model are studied. A two-stage method is presented to estimate the regression parameters and the coefficient functions. The asymptotic distributions of the proposed estimators are obtained. Meanwhile, a bias-corrected EL ratio for the regression parameters is proposed. It is shown that the ratio is asymptotically standard chi-squared. The result can be directly used to construct the EL confidence regions of the regression parameters. Simulation studies are carried out to evaluate the finite sample behaviour of the proposed method. An application example of a real data set is given.

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部分线性单指标变系数模型的两阶段估计和偏差校正经验似然

研究了部分线性单指标变系数模型中感兴趣参数的估计和经验似然。提出了一种两阶段估计回归参数和系数函数的方法。得到了所提估计量的渐近分布。同时，对回归参数提出了一种偏差校正后的EL比值。结果表明，该比值为渐近标准卡方。该结果可直接用于构建回归参数的EL置信区域。进行了仿真研究，以评估所提出的方法的有限样本行为。给出了一个实际数据集的应用实例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Royal Statistical Society Series B-Statistical Methodology 数学-统计学与概率论

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.