Small area estimation, and spatial statistics

点击翻译按钮获取中文摘要

Small area estimation and spatial statistical models have practical importance and theoretical interest in survey sampling and public health, respectively. Small area estimation has received considerable attention in recent years due to growing demand for reliable small area statistics that are needed in formulating policies and programs, allocation of government funds, regional planning, marketing decisions at local level, and other uses. Areas of application of small area estimation include poverty counts of school-age children at the area level, income for small places, monthly unemployment rates for Census Metropolitan Areas, and health-related estimates for local areas. Spatial statistics refer to a broad variety of techniques grounded in statistical theory that can identify clusters, compare rates among geographic area, separate systematic patterns from random noise, quantify uncertainty of measures (e.g., estimates), and assess potential predictors in a spatial context. While spatial statistics are particularly useful for population and public health, these methods can also be applied to diverse fields including astronomy, agriculture, and economics.