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一种半参数多重插补方法AI 解读

Summary In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non-standard problems, such as estimating the median when data are missing.

缺失数据下重复结局半参数回归模型的分析AI 解读

We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not…

使用二次指数模型的相关二元回归AI 解读

A model of quadratic exponential form is parameterized in terms of marginal means and pairwise correlations for the regression analysis of correlated binary data. Pseudo-maximum likelihood methods using a special case termed the multiplicative model are proposed, but are noted to be computationally unattractive if the ‘blocks’ of correlated responses are at all large. On the other hand score estimating functions for mean and correlation parameters are shown to be of a…