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统计等价模型下的似然推断AI 解读

Summary In likelihood inference we usually assume that the model is fixed and then base inference on the corresponding likelihood function. Often, however, the choice of model is rather arbitrary, and there may be other models which fit the data equally well. We study robustness of likelihood inference over such ‘statistically equivalent’ models and suggest a simple ‘envelope likelihood’ to capture this aspect of model uncertainty. Robustness depends critically on how we…

局部模型不确定性与不完整数据偏差（含讨论）AI 解读

Summary Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual…

2×2 表元分析中的发表偏差：平均蒙特卡洛 EM 算法AI 解读

Summary A major difficulty in meta-analysis is publication bias. Studies with positive outcomes are more likely to be published than studies reporting negative or inconclusive results. Correcting for this bias is not possible without making untestable assumptions. In this paper, a sensitivity analysis is discussed for the meta-analysis of 2×2 tables using exact conditional distributions. A Markov chain Monte Carlo EM algorithm is used to calculate maximum likelihood…

选择性偏差的局部敏感性近似AI 解读

Summary Observational data analysis is often based on tacit assumptions of ignorability or randomness. The paper develops a general approach to local sensitivity analysis for selectivity bias, which aims to study the sensitivity of inference to small departures from such assumptions. If M is a model assuming ignorability, we surround M by a small neighbourhood N defined in the sense of Kullback–Leibler divergence and then compare the inference for models in N with that for…

一类局部似然方法与近参数渐近理论AI 解读

Summary The local maximum likelihood estimate θ^ t of a parameter in a statistical model f(x, θ) is defined by maximizing a weighted version of the likelihood function which gives more weight to observations in the neighbourhood of t. The paper studies the sense in which f(t, θ^t) is closer to the true distribution g(t) than the usual estimate f(t, θ^) is. Asymptotic results are presented for the case in which the model misspecification becomes vanishingly small as the…