论文检索

误分类下流行率与趋势估计的贝叶斯方法AI 解读

Abstract In this paper, we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified, and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002–2014 National Survey on Drug Use and…

二元协变量误分类回归中的贝叶斯矩条件推断AI 解读

We present a Bayesian analysis of a regression model with a binary covariate that may have classification (measurement) error. Prior research demonstrates that the regression coefficient is only partially identified. We take a Bayesian approach which adds assumptions in the form of priors on the unknown misclassification probabilities. The approach is intermediate between the frequentist bounds of previous literature and strong assumptions which achieve point identification,…

空间自回归模型的有效 GMM 估计量AI 解读

In this paper, we consider GMM estimation of the regression and MRSAR models with SAR disturbances. We derive the best GMM estimator within the class of GMM estimators based on linear and quadratic moment conditions. The best GMM estimator has the merit of computational simplicity and asymptotic efficiency. It is asymptotically as efficient as the ML estimator under normality and asymptotically more efficient than the Gaussian QML estimator otherwise. Monte Carlo studies…

我没说，也不会说：SIPP中的动态回答误差AI 解读

Abstract Using state administrative records matched to the 1984 Survey of Income and Program Participation , we examine intertemporal relationships in response errors. False negative errors in reporting food stamps are highly correlated across interviews for the same household. Hypotheses that the error process can be explained by learning behaviour are not supported. Bivariate probit of response error in two periods reveals that responses to covariates are stable over time…