Maximum likelihood estimation in semiparametric selection bias models with application to AIDS vaccine trials
Maximum likelihood estimation in semiparametric selection bias models with application to AIDS vaccine trials
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DOI：
10.1093/biomet/86.1.27
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发表时间：
1999-03-01
期刊：
影响因子：
2.7
通讯作者：
Vardi, Y
中科院分区：
文献类型：
--
作者：
Gilbert, PB;Lele, SR;Vardi, Y
下面的问题被处理：给定s可能选择偏置的样本从一个未知的分布函数，并假设每个样本的抽样规则的权重函数是数学指定的一个共同的未知有限维参数，我们如何使用的数据来估计未知参数？我们提出了一个简单的最大偏似然方法来推导半参数最大似然估计。讨论的假设下，选择偏差模型是可识别的，唯一可估计的，我们激励需要的方法，通过讨论广义逻辑回归模型（吉尔伯特，自我和阿什比，1998年），半参数选择偏差模型，这是有用的评估从疫苗试验数据的HIV疫苗的疗效如何与暴露的病毒的特性。我们通过模拟和一个例子表明，广义Logistic回归模型的最大似然估计具有令人满意的有限样本性质。
The following problem is treated: given s possibly selection biased samples from an unknown distribution function, and assuming that the sampling rule weight functions for each of the samples are mathematically specified up to a common unknown finite-dimensional parameter, how can we use the data to estimate the unknown parameters? We propose a simple maximum partial likelihood method for deriving the semiparametric maximum likelihood estimator. A discussion of assumptions under which the selection bias model is identifiable and uniquely estimable is presented, We motivate the need for the methodology by discussing the generalised logistic regression model (Gilbert, Self & Ashby, 1998), a semiparametric selection bias model which is useful for assessing from vaccine trial data how the efficacy of an HIV vaccine varies with characteristics of the exposing virus. We show through simulations and an example that the maximum likelihood estimator in the generalised logistic regression model has satisfactory finite-sample properties.