Kernel regression for cause-specific hazard models with nonparametric covariate functions

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2023-04-14 DOI:10.1080/10485252.2023.2197088

Xiaomeng Qi, Zhangsheng Yu

引用次数: 1

Abstract

We study the local kernel pseudo-partial likelihood approach for the cause-specific hazard model with nonparametric covariate functions. The derivative of the covariate function is estimated first, and the estimator of the nonparametric covariate function is then derived by integrating the derivative estimator. The consistency and pointwise asymptotic normality of the local kernel estimator for the interested failure types are obtained. Moreover, numerical studies show that the proposed kernel estimator performs well under a finite sample size. And we compare the local kernel estimator with the regression B-splines estimator. We also apply the proposed method to analyse the kidney and renal pelvis cancer data with composite endpoints.

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具有非参数协变量函数的特定原因风险模型的核回归

研究了具有非参数协变量函数的原因特异性风险模型的局部核伪偏似然方法。首先估计协变量函数的导数，然后通过对导数估计量的积分得到非参数协变量函数的估计量。得到了感兴趣的失效类型的局部核估计的相合性和点渐近正态性。此外，数值研究表明，所提出的核估计器在有限样本容量下具有良好的性能。并将局部核估计量与回归b样条估计量进行了比较。我们还应用所提出的方法，以复合终点分析肾癌和肾盂癌的数据。

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 Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.