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线性混合模型中的一致推断AI 解读

Summary We provide finite-sample distribution approximations that are uniform in the parameter for inference in linear mixed models. The focus is on variances and covariances of random effects in cases where existing theory fails because the covariance matrix is nearly or exactly singular and hence near or at the boundary of the parameter set. Quantitative bounds on the differences between the standard normal density and densities of linear combinations of the score function…

奇异信息与参数边界附近的置信域及其混合模型应用AI 解读

We propose confidence regions with asymptotically correct uniform coverage probability of parameters whose Fisher information matrix can be singular at important points of the parameter set. Our work is motivated by the need for reliable inference on scale parameters close or equal to zero in mixed models, which is obtained as a special case. The confidence regions are constructed by inverting a continuous extension of the score test statistic standardized by expected…

纵向数据分位数回归系数函数的参数化建模AI 解读

In ordinary quantile regression, quantiles of different order are estimated one at a time. An alternative approach, which is referred to as quantile regression coefficients modeling (qrcm), is to model quantile regression coefficients as parametric functions of the order of the quantile. In this article, we describe how the qrcm paradigm can be applied to longitudinal data. We introduce a two-level quantile function, in which two different quantile regression models are used…

精度约束下用于推断的最优概率权重AI 解读

Probability weights are used in many areas of research including complex survey designs, missing data analysis, and adjustment for confounding factors. They are useful analytic tools but can lead to statistical inefficiencies when they contain outlying values. This issue is frequently tackled by replacing large weights with smaller ones or by normalizing them through smoothing functions. While these approaches are practical, they are also prone to yield biased inferences.…