Bayesian methods (1)

Chair: Serena Arima

This session contains four contributions of Bayesian methods to small area estimation. The talks give methodological developments and applications related to quantile and semi parametric regression, mapping environmental indicators and sample size determination.

Small area estimation based on quantile regression: a Bayesian approach

Quantile and M-quantile regression methods have been applied to small area estimation in several papers (we can quote Chambers and Tzavidis (2006) ; Chambers et al. (2014) among those). The main idea is that of using a semi-parametric regression model for quantiles, thus avoiding parametric distributional assumptions on regression’s residuals and random effects. Chambers and […]

Mapping of Arsenic Contamination in Ground Water: A New Hierarchical Bayesian Method

Arsenic (As) is a toxic metal commonly found in groundwater in many countries. Long term exposure to arsenic in food and water has been cited as a major health hazard. The maximum level of arsenic considered safe, set by the World Health Organization (WHO), is 10 mg/L. This is just a guideline — many countries […]

A Bayesian semi-parametric approach to small area estimation and forecasting: with application to estimating and forecasting mortality rates by country, age and sex.

Researchers and policymakers are often interested in estimating and forecasting rates cross-classified by several dimensions. We consider the case of simultaneously estimating and forecasting mortality rates, cross-classified by age, sex and country, for 40 countries in the Human Mortality Database. These rates have complicated interactions. For instance, age-sex profiles differ across countries, and are changing […]

Bayesian Sample Size Determination for Planning Hierarchical Bayes Small Area Estimates

This paper devises a fully Bayesian sample size determination method for hierarchical model-based small area estimation with a decision risk approach. A new loss function specified around a desired maximum posterior variance target implements conventional official statistics criteria of estimator reliability (coefficient of variation of up to 20 per cent). This approach comes with an […]

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