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利用 Fourier 表示进行半参数回归的贝叶斯推断AI 解读

Summary This paper presents the Bayesian analysis of a semiparametric regression model that consists of parametric and nonparametric components. The nonparametric component is represented with a Fourier series where the Fourier coefficients are assumed a priori to have zero means and to decay to 0 in probability at either algebraic or geometric rates. The rate of decay controls the smoothness of the response function. The posterior analysis automatically selects the amount…

预测不必要住院索赔比例的分层贝叶斯模型AI 解读

A nonacceptable claim (NAC) is an insurance claim for an unnecessary hospital stay. This study establishes a statistical model that predicts the NAC rate. The model supplements current insurer programs that rely on detailed audits of patient medical records. Hospital discharge claim records are used as inputs in the statistical model to predict retrospectively the probability that a hospital admission is nonacceptable. A full Bayesian hierarchical logistic regression model…

This paper reports the results of an empirical study of household brand choices over four scanner panel data sets. The study uses a random-effects, autocorrelated logistic regression model. The analysis presents evidence that the brand choice process is not zero-order. In addition, the influence of in-store displays and feature advertisements on switching is shown to be about two to three times more effective than estimates obtained from previous studies. Finally, the…

利用逻辑正态回归建模家庭购买行为AI 解读

The successful development of marketing strategies requires the accurate measurement of household preferences and their reaction to variables such as price and advertising. Manufacturers, for example, often offer products at a reduced price for a limited period. One reason for this practice is that it induces households to try the promoted product with the hope of retaining them as permanent customers. The successful implementation of this strategy requires knowledge of the…

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迈向可实施的贝叶斯非参数密度估计AI 解读

SUMMARY Nonparametric density estimators smooth the empirical distribution function and are sensitive to the choice of smoothing parameters. This paper develops an hierarchical Bayes formulation for the smoothing problem. The prior distribution for the density function is the logistic normal process, which is a logistic transform of a Gaussian process. The covariance of the Gaussian process is a smoothing kernel and has parameters that control the degree of smoothness. The…

用于贝叶斯非参数预测密度的逻辑斯蒂正态分布AI 解读

This article models the common density of an exchangeable sequence of observations by a generalization of the process derived from a logistic transform of a Gaussian process. The support of the logistic normal includes all distributions that are absolutely continuous with respect to the dominating measure of the observations. The logistic-normal family is closed in the prior to posterior Bayes analysis, with the observations entering the posterior distribution through the…

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