稀疏纵向协变量时变系数乘性风险模型的回归分析。

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2025-02-19 DOI:10.1080/10485252.2025.2466649

Zhuowei Sun, Hongyuan Cao

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引用次数: 0

摘要

研究了具有间断性观测纵向协变量和时变系数的乘法危险模型。对于这样的模型，现有的特别方法，比如最后一个值的结转，是有偏差的。我们提出了一种核加权方法来获得非参数系数函数的无偏估计，并建立了任意固定时间点的渐近正态性。此外，我们构建了同步置信带来检验变化的总体幅度。仿真研究支持了我们的理论预测，并表明了该方法的良好性能。一组来自阿尔茨海默病神经影像学倡议研究的数据被用来说明我们的方法。

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Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates.

We study the multiplicative hazards model with intermittently observed longitudinal covariates and time-varying coefficients. For such models, the existing ad hoc approach, such as the last value carried forward, is biased. We propose a kernel weighting approach to get an unbiased estimation of the non-parametric coefficient function and establish asymptotic normality for any fixed time point. Furthermore, we construct the simultaneous confidence band to examine the overall magnitude of the variation. Simulation studies support our theoretical predictions and show favorable performance of the proposed method. A data set from Alzheimer's Disease Neuroimaging Initiative study is used to illustrate our methodology.

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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.