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序贯治疗决策中最优动态治疗方案的稳健估计AI 解读

A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient's history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression…

重复次数可能不等且数据非正态时的测定分析方差函数估计AI 解读

Estimation of parametric variance functions using transformations of standard deviations based on replication at each design point is common in, but not limited to, assay analysis. It is shown that ignoring unequal replication can lead to bias and inefficiency in estimation. Efficiency comparisons for different transformations for nonnormal distributions are given. A method to account for bias is described that can offer robustness to nonnormality and leads to a comparison…

扩展拟似然方法的一则注记AI 解读

SUMMARY We study the method of extended quasi-likelihood estimation and inference of a variance function recently proposed by Nelder and Pregibon. The estimates are inconsistent in general, and the test levels can be biased, but in many cases such as the exponential family the inconsistency and bias will not be a major concern. Extended quasi-likelihood is compared with Carroll and Ruppert's pseudo-likelihood method, which gives consistent estimates and, when slightly…

测定分析中的方差函数与最低可检测浓度AI 解读

Assay data are often fitted by a nonlinear heteroscedastic regression model with the standard deviation of the response typically taken to be proportional to a power θ of the mean. For many assays, how one estimates θ does not greatly affect estimates of the mean regression function. Assay analysis also involves estimation of auxiliary calibration constructs such as minimum detectable concentration. An asymptotic theory is developed to show that standard methods for…

检测分析中的方差函数与最低可检测浓度AI 解读

Assay data are often fitted by a nonlinear heteroscedastic regression model with the standard deviation of the response typically taken to be proportional to a power θ of the mean. For many assays, how one estimates θ does not greatly affect estimates of the mean regression function. Assay analysis also involves estimation of auxiliary calibration constructs such as minimum detectable concentration. An asymptotic theory is developed to show that standard methods for…

方差函数估计AI 解读

Heteroscedastic regression models are used in fields including economics, engineering, and the biological and physical sciences. Often, the heteroscedasticity is modeled as a function of the covariates or the regression and other structural parameters. Standard asymptotic theory implies that how one estimates the variance function, in particular the structural parameters, has no effect on the first-order properties of the regression parameter estimates; there is evidence,…