论文检索

广义平滑有限混合模型AI 解读

We propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights. The component densities can belong to any parametric family, with each model parameter being a deterministic function of covariates through a link function. Our MCMC methodology allows…

使用平滑自适应高斯混合的回归密度估计AI 解读

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when…

随机效应异方差 Probit 模型的贝叶斯估计AI 解读

Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance…

纵向数据的简约协方差矩阵估计AI 解读

This article proposes a data-driven method to identify parsimony in the covariance matrix of longitudinal data and to exploit any such parsimony to produce a statistically efficient estimator of the covariance matrix. The approach parameterizes the covariance matrix through the Cholesky decomposition of its inverse. For longitudinal data, this is a one-step-ahead predictive representation, and the Cholesky factor is likely to have off-diagonal elements that are zero or close…

利用数据驱动先验进行加性非参数回归的变量选择与函数估计AI 解读

A hierarchical Bayesian approach is proposed for variable selection and function estimation in additive nonparametric Gaussian regression models and additive nonparametric binary regression models. The prior for each component function is an integrated Wiener process resulting in a posterior mean estimate that is a cubic smoothing spline. Each of the explanatory variables is allowed to be in or out of the model, and the regression functions are estimated by model averaging.…

稳健二元非参数回归的贝叶斯方法AI 解读

This article presents a Bayesian approach to binary nonparametric regression that assumes that the argument of the link is an additive function of the explanatory variables and their multiplicative interactions. The article makes the following contributions. First, a comprehensive approach is presented in which the function estimates are smoothing splines with the smoothing parameters integrated out and the estimates are made robust to outliers. Second, the approach can…

稳健二元非参数回归的贝叶斯方法AI 解读

This article presents a Bayesian approach to binary nonparametric regression that assumes that the argument of the link is an additive function of the explanatory variables and their multiplicative interactions. The article makes the following contributions. First, a comprehensive approach is presented in which the function estimates are smoothing splines with the smoothing parameters integrated out and the estimates are made robust to outliers. Second, the approach can…

处理组与对照组曲线估计的信号提取方法AI 解读

Suppose that the response of a group of subjects, which we call the control group, is monitored over time. At a given time after the start of the experiment, the experimenter intervenes and applies a treatment to a subset of the group. We propose a signal extraction approach to the simultaneous estimation of the treatment and control mean response curves, and their rates of change, imposing a smooth transition constraint between the two curves at the time of intervention. By…