Empirical Bayes Small-Area Estimation Using Logistic Regression Models and Summary Statistics |
| |
Authors: | Patrick J Farrell Brenda MacGibbon Thomas J Tomberlin |
| |
Institution: | 1. Department of Mathematics and Statistics , Acadia University , Wolfville , Nova Scotia , BOP 1X0 , Canada;2. Département de Mathématiques , Université du Québec à Montréal , Montréal , Québec , H3C 3P8 , Canada;3. Department of Decision Sciences and MIS , Concordia University , Montréal , Québec , H3G 1M8 , Canada |
| |
Abstract: | Many of the available methods for estimating small-area parameters are model-based approaches in which auxiliary variables are used to predict the variable of interest. For models that are nonlinear, prediction is not straightforward. MacGibbon and Tomberlin and Farrell, MacGibbon, and Tomberlin have proposed approaches that require microdata for all individuals in a small area. In this article, we develop a method, based on a second-order Taylor-series expansion to obtain model-based predictions, that requires only local-area summary statistics for both continuous and categorical auxiliary variables. The methodology is evaluated using data based on a U.S. Census. |
| |
Keywords: | Bootstrap Labor-force participation Random-effects models Taylor-series approximation |
|
|