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简便的放松条件似然方法AI 解读

When the data are sparse but not exceedingly so, we face a trade-off between bias and precision that makes the usual choice between conducting either a fully unconditional inference or a fully conditional inference unduly restrictive. We propose a method to relax the conditional inference that relies upon commonly available computer outputs. In the rectangular array asymptotic setting, the relaxed conditional maximum likelihood estimator has smaller bias than the…

缺失数据估计：超越双重稳健性AI 解读

We propose an estimator that is more robust than doubly robust estimators, based on weighting complete cases using weights other than inverse probability when estimating the population mean of a response variable subject to ignorable missingness. We allow multiple models for both the propensity score and the outcome regression. Our estimator is consistent if any of the multiple models is correctly specified. Such multiple robustness against model misspecification is a…

合并纵向研究的二次推断函数方法：验证与联合估计AI 解读

Merging data from multiple studies has been widely adopted in biomedical research. In this paper, we consider two major issues related to merging longitudinal datasets. We first develop a rigorous hypothesis testing procedure to assess the validity of data merging, and then propose a flexible joint estimation procedure that enables us to analyse merged data and to account for different within-subject correlations and follow-up schedules in different studies. We establish…

平方根 lasso：通过锥规划实现枢轴式稀疏信号恢复AI 解读

We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are significant. The method is a modification of the lasso, called the square-root lasso. The method is pivotal in that it neither relies on the knowledge of the standard deviation σ nor does it need to pre-estimate σ. Moreover, the method does not rely on normality or…

贝叶斯保序密度回归AI 解读

Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not clear whether such priors have full support so that any true data-generating model…

Wilcoxon 型广义贝叶斯信息准则AI 解读

We develop a generalized Bayesian information criterion for regression model selection. The new criterion relaxes the usually strong distributional assumption associated with Schwarz's bic by adopting a Wilcoxon-type dispersion function and appropriately adjusting the penalty term. We establish that the Wilcoxon-type generalized bic preserves the consistency of Schwarz's bic without the need to assume a parametric likelihood. We also show that it outperforms Schwarz's bic…