Integrated Modeling of European Migration
欧洲国际迁移的综合建模AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要核心是迁移数据的贝叶斯整合,虽覆盖多国多年,但未明确采用指定宏观面板方法。
欧洲国际迁移的综合建模AI 解读 ↓
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International migration data in Europe are collected by individual countries with separate collection systems and designs. As a result, reported data are inconsistent in availability, definition, and quality. In this article, we propose a Bayesian model to overcome the limitations of the various data sources. The focus is on estimating recent international migration flows among 31 countries in the European Union and European Free Trade Association from 2002 to 2008, using data collated by Eurostat. We also incorporate covariate information and information provided by experts on the effects of undercount, measurement, and accuracy of data collection systems. The methodology is integrated and produces a synthetic database with measures of uncertainty for international migration flows and other model parameters. Supplementary materials for this article are available online.
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…
自适应实验中策略改进价值的推断
In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, which is defined as the gap between the value of the best treatment policy in a class and that of a baseline policy, gauges whether or not it is worth deploying a personalized treatment policy. Building an estimator for the VPI is naturally more difficult than estimating the value of a fixed policy. This is because, in addition…