Results 1 to 10 of about 1,760,812 (299)
Smoothed model-assisted small area estimation of proportions. [PDF]
Can J Stat, 2022 In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging.
Gao PA, Wakefield J.
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Crime against women in India: district-level risk estimation using the small area estimation approach [PDF]
Frontiers in Public HealthBackgroundThe global prevalence of crimes against women has made it an enduring public health challenge that has persisted over time. The achievement of the 2030 Sustainable Development Goal (SDG) is intricately tied to the actions taken to prevent these
B. S. Pooja +2 more
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PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation. [PDF]
J R Soc Interface, 2022 Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling.
Semenova E +6 more
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A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands. [PDF]
Int J Health Geogr, 2022 Background Local policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being of ...
Viljanen M +3 more
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Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory
Frontiers in Forests and Global Change, 2022 Small area estimation is a growing area of research for making inferences over geographic, demographic, or temporal domains smaller than those in which a particular survey data set was originally intended to be used.
Philip J Radtke +2 more
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Small Area Estimation for Disease Prevalence Mapping. [PDF]
Int Stat Rev, 2020 Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available.
Wakefield J, Okonek T, Pedersen J.
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Data-Driven Transformations in Small Area Estimation [PDF]
Journal of the Royal Statistical Society Series A: Statistics in Society, 2019 SummarySmall area models typically depend on the validity of model assumptions. For example, a commonly used version of the empirical best predictor relies on the Gaussian assumptions of the error terms of the linear mixed regression model: a feature rarely observed in applications with real data. The paper tackles the potential lack of validity of the
Rojas-Perilla, N. +3 more
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Small Area Estimation with Linked Data [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2020 AbstractData linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a population register, for use in small area estimation. However, linkage errors can induce bias when fitting regression models; moreover, they can create non-representative ...
Salvati N. +3 more
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Multivariate Global-Local Priors for Small Area Estimation
Stats, 2022 It is now widely recognized that small area estimation (SAE) needs to be model-based. Global-local (GL) shrinkage priors for random effects are important in sparse situations where many areas’ level effects do not have a significant impact on the ...
Tamal Ghosh +3 more
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Small Area Quantile Estimation [PDF]
International Statistical Review, 2018 SummarySample surveys are widely used to obtain information about totals, means, medians and other parameters of finite populations. In many applications, similar information is desired for subpopulations such as individuals in specific geographic areas and socio‐demographic groups. When the surveys are conducted at national or similarly high levels, a
Jiahua Chen, Yukun Liu
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