Semiparametric Inference for Proportional Mean Past Life Model.

IF 1.2 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2019-05-18 DOI:10.1515/ijb-2018-0020

Z Mansourvar, M Asadi

引用次数: 1

Abstract

The mean past lifetime provides the expected time elapsed since the failure of a subject given that he/she has failed before the time of observation. In this paper, we propose the proportional mean past lifetime model to study the association between the mean past lifetime function and potential regression covariates. In the presence of left censoring, martingale estimating equations are developed to estimate the model parameters, and the asymptotic properties of the resulting estimators are studied. To assess the adequacy of the model, a goodness of fit test is also investigated. The proposed method is evaluated via simulation studies and further applied to a data set.

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比例平均过去寿命模型的半参数推断。

平均过去寿命提供了自被试失败后经过的预期时间，假设他/她在观察时间之前已经失败。在本文中，我们提出了比例平均过去寿命模型来研究平均过去寿命函数与潜在回归协变量之间的关系。在存在左删减的情况下，建立了估计模型参数的鞅估计方程，并研究了所得估计量的渐近性质。为了评估模型的充分性，还研究了拟合优度检验。通过仿真研究对所提出的方法进行了评估，并进一步应用于数据集。

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

International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.