Regression analysis under non-standard situations: a pairwise pseudolikelihood approach
Regression analysis under non-standard situations: a pairwise pseudolikelihood approach
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DOI：
10.1111/1467-9868.00263
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发表时间：
2000-01-01
影响因子：
5.8
通讯作者：
Qin, J
中科院分区：
文献类型：
--
作者：
Liang, KY;Qin, J
回归分析是数据分析中最常用的统计方法之一。然而，在许多情况下，我们不能仅根据 f(y \ x; beta) 进行推理，f(y \ x; beta) 是响应变量 Y 的条件概率（密度）函数，给定 x（协变量）。例如，缺失数据（缺失是不可忽略的）、抽样调查（根据 Y 值选择受试者）和荟萃分析（已发表的研究会受到“选择偏差”的影响）。传统方法需要分别正确指定缺失机制、抽样概率和发布概率。在本文中，我们基于 Kalbfleisch 提出的想法提出了一种替代的 beta 估计程序。该方法的新颖之处在于不需要指定对上述缺失概率机制等的假设。进行了渐近效率计算和模拟研究，以将所提出的方法与两种现有方法（条件似然法和加权估计函数方法）进行比较。
Regression analysis is one of the most used statistical methods for data analysis. There are, however, many situations in which one cannot base inference solely on f(y \ x; beta), the conditional probability (density) function for the response variable Y, given x, the covariates. Examples include missing data where the missingness is non-ignorable, sampling surveys in which subjects are selected on the basis of the Y-values and meta-analysis where published studies are subject to 'selection bias'. The conventional approaches require the correct specification of the missingness mechanism, sampling probability and probability for being published respectively. In this paper, we propose an alternative estimating procedure for beta based on an idea originated by Kalbfleisch. The novelty of this method is that no assumption on the missingness probability mechanisms etc. mentioned above is required to be specified. Asymptotic efficiency calculations and simulation studies were conducted to compare the method proposed with the two existing methods: the conditional likelihood and the weighted estimating function approaches.