Small Area Estimation for Disease Prevalence Mapping

Autor:	Jon Pedersen, Taylor Okonek, Jonathan Wakefield
Rok vydání:	2020
Předmět:	Statistics and Probability business.industry Computer science 05 social sciences Bayesian probability Prevalence Inference Machine learning computer.software_genre 01 natural sciences Article Variety (cybernetics) Weighting 010104 statistics & probability Small area estimation 0502 economics and business Covariate Range (statistics) Artificial intelligence 0101 mathematics Statistics Probability and Uncertainty business computer 050205 econometrics
Zdroj:	Int Stat Rev
ISSN:	1751-5823 0306-7734
Popis:	Summary Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015?2016, is used to illustrate a number of techniques The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae0c0fe46db5072890f6d0f923785f8a https://doi.org/10.1111/insr.12400 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.