Analysis

The National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is responsible for estimating average cash rental rates at the county level. A cash rental rate refers to the market value of land rented on a per acre basis for cash only. Estimates of cash rental rates are useful to farmers, economists, and policy makers. NASS collects data on cash rental rates using a Cash Rent Survey. Because realized sample sizes at the county level are often too small to support reliable direct estimators, predictors based on mixed models are investigated. We specify a bivariate model to obtain predictors of 2010 cash rental rates for non-irrigated cropland using data from the 2009 Cash Rent Survey and auxiliary variables from external sources such as the 2007 Census of Agriculture. We use Bayesian methods for inference and present results for Iowa, Kansas, and Texas. Incorporating the 2009 survey data through a bivariate model leads to predictors with smaller mean squared errors than predictors based on a univariate model.

Small area models handling area level data typically assume normality of random effects. This assumption does not always work. The present paper introduces a new small area model with t random effects. Along with this, this paper also considers joint modeling of small area means and variances. The present approach is shown to perform better than other methods.

The paper considers small domain estimation of the proportion of persons without health insurance for different minority groups. The small domains are cross-classified by age, sex and other demographic characteristics. Both hierarchical and empirical Bayes estimation methods are used. Also, second order accurate approximations of the mean squared errors of the empirical Bayes estimators and bias-corrected estimators of these mean squared errors are provided. The general methodology is illustrated with estimates of the proportion of uninsured persons for several cross-sections of the Asian subpopulation.

The present article discusses a model-based approach towards adjustment of the 1988 Census Dress Rehearsal Data collected from test sites in Missouri. The primary objective is to develop procedures that can be used to model data from the 1990 Census Post Enumeration Survey in April, 1991 and smooth survey-based estimates of the adjustment factors. We have proposed in this paper hierarchical Bayes (HB) and empirical Bayes (EB) procedures which meet this objective. The resulting estimators seem to improve consistently on the estimators of the adjustment factors based on dual system estimation (DSE) as well as the smoothed regression estimators.

The National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is responsible for estimating average cash rental rates at the county level. A cash rental rate refers to the market value of land rented on a per acre basis for cash only. Estimates of cash rental rates are useful to farmers, economists, and policy makers. NASS collects data on cash rental rates using a Cash Rent Survey. Because realized sample sizes at the county level are often too small to support reliable direct estimators, predictors based on mixed models are investigated. We specify a bivariate model to obtain predictors of 2010 cash rental rates for non-irrigated cropland using data from the 2009 Cash Rent Survey and auxiliary variables from external sources such as the 2007 Census of Agriculture. We use Bayesian methods for inference and present results for Iowa, Kansas, and Texas. Incorporating the 2009 survey data through a bivariate model leads to predictors with smaller mean squared errors than predictors based on a univariate model.

Small area models handling area level data typically assume normality of random effects. This assumption does not always work. The present paper introduces a new small area model with t random effects. Along with this, this paper also considers joint modeling of small area means and variances. The present approach is shown to perform better than other methods.

The paper considers small domain estimation of the proportion of persons without health insurance for different minority groups. The small domains are cross-classified by age, sex and other demographic characteristics. Both hierarchical and empirical Bayes estimation methods are used. Also, second order accurate approximations of the mean squared errors of the empirical Bayes estimators and bias-corrected estimators of these mean squared errors are provided. The general methodology is illustrated with estimates of the proportion of uninsured persons for several cross-sections of the Asian subpopulation.

The present article discusses a model-based approach towards adjustment of the 1988 Census Dress Rehearsal Data collected from test sites in Missouri. The primary objective is to develop procedures that can be used to model data from the 1990 Census Post Enumeration Survey in April, 1991 and smooth survey-based estimates of the adjustment factors. We have proposed in this paper hierarchical Bayes (HB) and empirical Bayes (EB) procedures which meet this objective. The resulting estimators seem to improve consistently on the estimators of the adjustment factors based on dual system estimation (DSE) as well as the smoothed regression estimators.