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摘要翻译：
多变量失效时间数据的变系数边际危险模型的统计估计和推断是生存分析中的重要课题。提出了一种估计未知系数函数的局部伪部分似然方法。为了提高估计器的效率，还提出了一种加权平均估计器。建立了估计量的相合性和渐近正态性，导出了估计系数的标准误差公式，并进行了实证检验。为了减少最大局部伪部分似然估计器的计算量，提出了一种简单实用的一步估计器。建立了一步估计器的统计特性，并进行了仿真研究，比较了一步估计器与最大局部伪部分似然估计器的性能。结果表明，一步估计可以在不影响渐近和经验性能的前提下节省计算量，最优加权平均估计比最大局部伪部分似然估计更有效。Busselton人口健康调查的一个数据集被分析来说明我们提出的方法。
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英文标题：
《Hazard models with varying coefficients for multivariate failure time
data》
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作者：
Jianwen Cai, Jianqing Fan, Haibo Zhou, Yong Zhou
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最新提交年份：
2007
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency and asymptotic normality of the proposed estimators are established and standard error formulas for the estimated coefficients are derived and empirically tested. To reduce the computational burden of the maximum local pseudo-partial likelihood estimator, a simple and useful one-step estimator is proposed. Statistical properties of the one-step estimator are established and simulation studies are conducted to compare the performance of the one-step estimator to that of the maximum local pseudo-partial likelihood estimator. The results show that the one-step estimator can save computational cost without compromising performance both asymptotically and empirically and that an optimal weighted average estimator is more efficient than the maximum local pseudo-partial likelihood estimator. A data set from the Busselton Population Health Surveys is analyzed to illustrate our proposed methodology.
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PDF链接：
https://arxiv.org/pdf/708.0519
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