Some Prior and Posterior Distributions in Survival Analysis: A Critical Insight on Relationships Derived from Cross-Sectional Data
生存分析中的若干先验与后验分布:对横截面关系的审视AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要聚焦生存选择与生理特征的横截面解释,未明确提出因果效应、面板估计或所列其他方向的计量方法。
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Summary Many physiological characteristics such as, for example, blood pressure, level of serum cholesterol, vital capacity, may be considered as ageing factors contributing to the overall hazard rate function in an additive fashion (formula (3.2)). If so, the regression function of the characteristic on time, in a population of survivors to time t, depends on t even if the characteristic, itself, does not vary with t, for any one individual. Also, if there is a relationship between a characteristic and age for an individual, it cannot be uniquely estimated from population cross-sectional data, unless its functional contribution to the hazard rate is determined. Special consideration is given to the analysis, when the prior (i.e. at t = 0) distribution of each characteristic is normal, and the characteristics are independent.
RBF 核高斯过程最大似然估计的精确渐近理论
Gaussian processes (GPs) are widely used across machine learning, spatial statistics, time-series analysis, optimization, Bayesian statistics, and scientific applications. A central component of a GP model is its kernel, which is typically specified through a parametric family. Among the most widely used choices is the radial basis function (RBF), also known as the squared exponential or Gaussian kernel, owing to its simple form, smoothness, and flexibility. In practice, the…
Bayesian additive regression trees (BART) is a popular Bayesian ensemble model used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as weak learners. In this work, we propose a generalization of the BART model that has two main features: first, it automatically selects the number of decision trees using the given data; second, the model allows clusters of…
估计纵向因果剂量反应关系的贝叶斯框架
Existing causal methods for time-varying exposure and time-varying confounding focus on estimating the average causal effect of a time-varying binary treatment on an end-of-study outcome, offering limited tools for characterizing marginal causal dose-response relationships under continuous exposures. We propose a scalable, nonparametric Bayesian framework for estimating marginal longitudinal causal dose-response functions with repeated outcome measurements. Our approach…
内生性检验:一种基于矩的贝叶斯方法
A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this article, using the exponentially tilted empirical likelihood (ETEL) framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity…
凸正则化的谱依赖:右旋转不变设计下的基本极限
We study the fundamental limits of convex-regularized estimation in high-dimensional linear regression with right-rotationally invariant design matrices. We show that the asymptotic risks of convex-penalized least squares estimators are lower bounded by the risk of an approximate message passing algorithm known as Bayes VAMP, and we further characterize when the lower bound is attainable. As a technical ingredient in the proof of our lower bound theorem, we characterize the…
We study structural changes in multivariate time series by estimating and comparing stationary graphs of macroeconomic time series before and after an economic crisis, such as the Great Recession. Building on a latent time series framework called Orthogonally-rotated Univariate Time-series (OUT), we propose a shared-parameter framework-the spOUT autoregressive model (spOUTAR)-that jointly models two related multivariate time series and enables coherent Bayesian estimation of…