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利用层级似然处理缺失数据问题AI 解读

Most statistical solutions to the problem of statistical inference with missing data involve integration or expectation. This can be done in many ways: directly or indirectly, analytically or numerically, deterministically or stochastically. Missing-data problems can be formulated in terms of latent random variables, so that hierarchical likelihood methods of Lee & Nelder (1996) can be applied to missing-value problems to provide one solution to the problem of integration of…

具有恰当时间依赖结构的模式混合模型AI 解读

Recently, pattern‐mixture modelling has become a popular tool for modelling incomplete longitudinal data. Such models are under‐identified in the sense that, for any drop‐out pattern, the data provide no direct information on the distribution of the unobserved outcomes, given the observed ones. One simple way of overcoming this problem, ordinary extrapolation of sufficiently simple pattern‐specific models, often produces rather unlikely descriptions; several authors consider…