Temat: area level model - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Comparison of Small Area Estimation Methods for Poverty Mapping
Autorzy:: Guadarrama, María
Molina, Isabel
Rao, J. N. K.
Powiązania:: https://bibliotekanauki.pl/articles/465671.pdf
Data publikacji:: 2016
Wydawca:: Główny Urząd Statystyczny
Tematy:: area level model
non-linear parameters
empirical best estimator
hierarchical Bayes
poverty mapping
unit level models
Opis:: We review main small area estimation methods for the estimation of general nonlinear parameters focusing on FGT family of poverty indicators introduced by Foster, Greer and Thorbecke (1984). In particular, we consider direct estimation, the Fay-Herriot area level model (Fay and Herriot, 1979), the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies.
Źródło:: Statistics in Transition new series; 2016, 17, 1; 41-66
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Borrowing Information over time in Binomial/Logit Normal Models for Small Area Estimation
Autorzy:: Franco, Carolina
Bell, William R.
Powiązania:: https://bibliotekanauki.pl/articles/465889.pdf
Data publikacji:: 2015
Wydawca:: Główny Urząd Statystyczny
Tematy:: area level model
complex surveys
American Community Survey
bivariate
model
SAIPE
Opis:: Linear area level models for small area estimation, such as the Fay-Herriot model, face challenges when applied to discrete survey data. Such data commonly arise as direct survey estimates of the number of persons possessing some characteristic, such as the number of persons in poverty. For such applications, we examine a binomial/logit normal (BLN) model that assumes a binomial distribution for rescaled survey estimates and a normal distribution with a linear regression mean function for logits of the true proportions. Effective sample sizes are defined so variances given the true proportions equal corresponding sampling variances of the direct survey estimates. We extend the BLN model to bivariate and time series (first order autoregressive) versions to permit borrowing information from past survey estimates, then apply these models to data used by the U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program to predict county poverty for school-age children. We compare prediction results from the alternative models to see how much the bivariate and time series models reduce prediction error variances from those of the univariate BLN model. Standard conditional variance calculations for corresponding linear Gaussian models that suggest how much variance reduction will be achieved from borrowing information over time with linear models agree generally with the BLN empirical results.
Źródło:: Statistics in Transition new series; 2015, 16, 4; 563-584
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Inferential Issues in Model-Based Small Area Estimation: Some New Developments
Autorzy:: Rao, J. N. K.
Powiązania:: https://bibliotekanauki.pl/articles/465725.pdf
Data publikacji:: 2015
Wydawca:: Główny Urząd Statystyczny
Tematy:: area level models
complex parameters
informative sampling
model misspecification
robust estimation
unit level models
Opis:: Small area estimation (SAE) has seen a rapid growth over the past 10 years or so. Earlier work is covered in the author's book (Rao 2003). The main purpose of this paper is to highlight some new developments in model-based SAE since the publication of the author's book. A large part of the new theory addressed practical issues associated with the model-based approach, and we present some of those methods for area level and unit level models. We also briefly mention some new work on synthetic estimation of area means or totals based on implicit models.
Źródło:: Statistics in Transition new series; 2015, 16, 4; 491-510
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki