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空间曲率过程的贝叶斯建模AI 解读

Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as wombling boundaries,…

多位点、多种群样本中单核苷酸多态性多样性的贝叶斯层级模型AI 解读

The distribution of genetic variation among populations is conveniently measured by Wright's F(ST), which is a scaled variance taking on values in [0,1]. For certain types of genetic markers, and for single-nucleotide polymorphisms (SNPs) in particular, it is reasonable to presume that allelic differences at most loci are selectively neutral. For such loci, the distribution of genetic variation among populations is determined by the size of local populations, the pattern and…

二元响应数据的一类新偏斜链接模型AI 解读

The logit, probit, and student t-links are widely used in modeling dichotomous quantal response data. Most of the commonly used link functions are symmetric, except the complementary log-log link. However, in some applications the overall fit can be significantly improved by the use of an asymmetric link. In this article we propose a new skewed link model for analyzing binary response data with covariates. Introducing a skewed distribution for the underlying latent variable,…

生存数据的半参数贝叶斯分析AI 解读

This review article investigates the potential of Bayes methods for the analysis of survival data using semiparametric models based on either the hazard or the intensity function. The nonparametric part of every model is assumed to be a realization of a stochastic process. The parametric part, which may include a regression parameter or a parameter quantifying the heterogeneity of a population, is assumed to have a prior distribution with possibly unknown hyperparameters.…

存在干扰参数时后验分位数的频率学有效性：高阶渐近分析AI 解读

Given a random sample from a distribution with density function that depends on an unknown parameter θ = (θ1, θ2), we are concerned with frequentist validity, up to o(n−1), of posterior quantiles of θ1, treating θ2 as a nuisance parameter. We propose to make the best choice of the prior on θ by matching, as far as practicable, the posterior and frequentist coverage probabilities up to o(n−1).

存在干扰参数时后验分位数的频率学有效性：高阶渐近分析AI 解读

Given a random sample from a distribution with density function that depends on an unknown parameter θ = (θ1, θ2), we are concerned with frequentist validity, up to o(n−1), of posterior quantiles of θ1, treating θ2 as a nuisance parameter. We propose to make the best choice of the prior on θ by matching, as far as practicable, the posterior and frequentist coverage probabilities up to o(n−1).

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Stein 损失下的协方差矩阵估计AI 解读

Stein's general technique for improving upon the best invariant unbiased and minimax estimators of the normal covariance matrix is described. The technique is to obtain solutions to a certain differential inequality involving the eigenvalues of the sample covariance matrix. Several improved estimators are obtained by solving the differential inequality. These estimators shrink or expand the sample eigenvalues depending on their magnitude. A scale invariant, adaptive minimax…

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