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Chatterjee 秩相关系数的简单 bootstrapAI 解读

Abstract We prove that an m-out-of-n bootstrap procedure for Chatterjee’s rank correlation is consistent whenever asymptotic normality of Chatterjee’s rank correlation can be established. In particular, we prove that m-out-of-n bootstrap works for continuous as well as discrete data with independent coordinates; furthermore, simulations indicate that it also performs well for discrete data with dependent coordinates, and that it outperforms alternative estimation methods.…

频率学派模型平均的最优实验设计AI 解读

Summary We consider the problem of designing experiments for estimating a target parameter in regression analysis when there is uncertainty about the parametric form of the regression function. A new optimality criterion is proposed that chooses the experimental design to minimize the asymptotic mean squared error of the frequentist model averaging estimate. Necessary conditions for the optimal solution of a locally and Bayesian optimal design problem are established. The…

半参数模型的最优区分设计AI 解读

Much work on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters. Recent work allows errors in the models to be nonnormally distributed but still requires the specification of the mean structures. Otsu (2008) proposed optimal discriminating designs for semiparametric models by generalizing the Kullback–Leibler optimality criterion proposed by López-Fidalgo et al. (2007). This paper develops a relatively simple…

疗效与毒性结局下活性对照剂量探索试验的最优设计AI 解读

We derive optimal designs to estimate efficacy and toxicity in active controlled dose-finding trials when the bivariate continuous outcomes are described using nonlinear regression models. We determine upper bounds on the required number of different doses and provide conditions under which the boundary points of the design space are included in the optimal design. We provide an analytical description of minimally supported optimal designs and show that they do not depend on…

基于预测方差的回归模型最优设计准则AI 解读

In the context of nonlinear regression models, a new class of optimum design criteria is developed and illustrated. This new class, termed ΙL-optimality, is analogous to Kiefer's Φk-criterion but is based on predicted variance, whereas Kiefer's class is based on the eigenvalues of the information matrix; L-optimal designs are invariant with respect to different parameterisations of the model and contain G- and D- optimality as special cases. We provide a general equivalence…

异方差多项式模型中固定设计点数的贝叶斯 D 最优设计AI 解读

We consider design issues in a polynomial regression model where the variance of the response depends on the independent variable exponentially. However, this dependence is not known precisely and additional parameters are required in the model. Our design criteria permit various subsets of the parameters to be estimated with different emphasis. Bayesian D-optimal designs on a compact interval, with the number of support points restricted to be one more than the degree of…