Special Issue Information

Dear Colleagues,

Recent advances in statistical methodology, coupled with heightened demand for disaggregated estimates, have stimulated the field of small area estimation to expand in diverse directions. Spatial–temporal and multivariate models capture dependence among areas and variables. Robust estimators and generalized linear mixed models accommodate non-normal distributions in unit-level responses. Semi/non-parametric model components allow for further flexibility in describing distributional forms and relations to covariates. Variable selection methods and measurement error models aid the use of increasingly complex auxiliary data sources. Methods for nonresponse and informative sampling guard against biased predictors when selection mechanisms are related to the characteristic of interest. The bootstrap and Bayesian procedures facilitate construction of accurate prediction intervals and mean square error estimators, even when models are complex and parameters are nonlinear functions of response variables. Developments in statistical methods have benefited salient applications that rely on small area estimates.

For this Special Issue, I welcome papers that develop innovative small area methods or that demonstrate a sound application of small area estimation to a problem of practical interest. Submissions related to a broad range of methods and applications are encouraged. I look forward to receiving your submission.

Dr. Emily Berg
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Stats is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

dependent data
nonlinear model
non-parametric
hierarchical
nonresponse
informative sampling
Bayesian
bootstrap
proportions
benchmarking

Published Papers (1 paper)

Research

Open AccessArticle

A Bayesian Approach to Linking a Survey and a Census via Small Areas

Balgobin Nandram

Stats 2021, 4(2), 509-528; https://doi.org/10.3390/stats4020031 - 09 Jun 2021

Viewed by 560

Abstract

We predict the finite population proportion of a small area when individual-level data are available from a survey and more extensive household-level (not individual-level) data (covariates but not responses) are available from a census. The census and the survey consist of the same strata and primary sampling units (PSU, or wards) that are matched, but the households are not matched. There are some common covariates at the household level in the survey and the census and these covariates are used to link the households within wards. There are also covariates at the ward level, and the wards are the same in the survey and the census. Using a two-stage procedure, we study the multinomial counts in the sampled households within the wards and a projection method to infer about the non-sampled wards. This is accommodated by a multinomial-Dirichlet–Dirichlet model, a three-stage hierarchical Bayesian model for multinomial counts, as it is necessary to account for heterogeneity among the households. The key theoretical contribution of this paper is to develop a computational algorithm to sample the joint posterior density of the multinomial-Dirichlet–Dirichlet model. Specifically, we obtain samples from the distributions of the proportions for each multinomial cell. The second key contribution is to use two projection procedures (parametric based on the nested error regression model and non-parametric based on iterative re-weighted least squares), on these proportions to link the survey to the census, thereby providing a copy of the census counts. We compare the multinomial-Dirichlet–Dirichlet (heterogeneous) model and the multinomial-Dirichlet (homogeneous) model without household effects via these two projection methods. An example of the second Nepal Living Standards Survey is presented. Full article

(This article belongs to the Special Issue Small Area Estimation: Models and Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Special Issue "Small Area Estimation: Models and Applications"

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (1 paper)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI