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Sampling Methods

ON ASYMMETRY OF PREDICTION ERRORS IN SMALL AREA ESTIMATION

The mean squared error reflects only the average prediction accuracy while the distribution of squared prediction error is positively skewed. Hence, assessing or comparing accuracy based on the MSE (which is the mean of squared errors) is insufficient and even inadequate because we should be interested not only in the average but in the whole distribution of prediction errors. This is the reason why we propose to use different than MSE measures of prediction accuracy in small area estimation

Tomasz Żądło

Statistics in Transition New Series , ISSUE 3, 413–432

Article

SMALL AREA ESTIMATION IN THE GERMAN CENSUS 2011

census, the total population counts. Further, the design should also adequately support the application of small area estimation methods. Some empirical results are given to provide an assessment of selected methods. The research was conducted within the German Census Sampling and Estimation research project, financially supported by the German Federal Statistical Office.

Ralf Münnich, Jan Pablo Burgard, Siegfried Gabler, Matthias Ganninger, Jan-Philipp Kolb

Statistics in Transition New Series , ISSUE 1, 25–40

Research paper

SAE EDUCATION CHALLENGES TO ACADEMICS AND NSI

The aim of the paper is to present some experiences in teaching Small Area Estimation (SAE). SAE education experiences and challenges are analysed from the academic side and from the NSI side. An attempt was undertaken to discuss SAE issues in a wider perspective of teaching statistics. In particular, the topics refer to Polish conditions, but they are presented against the background of selected international experiences and practices. Information comes from a special inquiry - a survey

Elżbieta Gołata

Statistics in Transition New Series , ISSUE 4, 611–630

Research paper

SAE TEACHING USING SIMULATIONS

The increasing interest in applying small area estimation methods urges the needs for training in small area estimation. To better understand the behaviour of small area estimators in practice, simulations are a feasible way for evaluating and teaching properties of the estimators of interest. By designing such simulation studies, students gain a deeper understanding of small area estimation methods. Thus, we encourage to use appropriate simulations as an additional interactive tool in teaching

Jan Pablo Burgard, Ralf Münnich

Statistics in Transition New Series , ISSUE 4, 603–610

Article

Small area estimation: its evolution in five decades

The paper is an attempt to trace some of the early developments of small area estimation. The basic papers such as the ones by Fay and Herriott (1979) and Battese, Harter and Fuller (1988) and their follow-ups are discussed in some details. Some of the current topics are also discussed.

Malay Ghosh

Statistics in Transition New Series , ISSUE 4, 1–22

Research paper

SMALL AREA ESTIMATION FOR SKEWED DATA IN THE PRESENCE OF ZEROES

Forough Karlberg

Statistics in Transition New Series , ISSUE 4, 541–562

Article

SMALL AREA ESTIMATION OF INCOME UNDER SPATIAL SAR MODEL

Jan Kubacki, Alina Jędrzejczak

Statistics in Transition New Series , ISSUE 3, 365–390

Research paper

AN APPROXIMATION TO THE OPTIMAL SUBSAMPLE ALLOCATION FOR SMALL AREAS

This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results

W. B. Molefe, D. K. Shangodoyin, R. G. Clark

Statistics in Transition New Series , ISSUE 2, 163–182

Article

A COMPARISON OF SMALL AREA ESTIMATION METHODS FOR POVERTY MAPPING

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

María Guadarrama, Isabel Molina, J. N. K. Rao

Statistics in Transition New Series , ISSUE 1, 41–66

Research paper

INFERENTIAL ISSUES IN MODEL-BASED SMALL AREA ESTIMATION: SOME NEW DEVELOPMENTS

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

J. N. K. Rao

Statistics in Transition New Series , ISSUE 4, 491–510

Research paper

COVARIATE SELECTION FOR SMALL AREA ESTIMATION IN REPEATED SAMPLE SURVEYS

If the implementation of small area estimation methods to multiple editions of a repeated sample survey is considered, then the question arises which covariates to use in the models. Applying standard model selection procedures independently to the different editions of the survey may identify different sets of covariates for each edition. If the small area predictions are sensitive to the different models, this is undesirable in official statistics since monitoring change over time of

Jan A. van den Brakel, Bart Buelens

Statistics in Transition New Series , ISSUE 4, 523–540

Research paper

Triple-goal estimation of unemployment rates for U.S. states using the U.S. Current Population Survey data

In this paper, we first develop a triple-goal small area estimation methodology for simultaneous estimation of unemployment rates for U.S. states using the Current Population Survey (CPS) data and a two-level random sampling variance normal model. The main goal of this paper is to illustrate the utility of the triple-goal methodology in generating a single series of unemployment rate estimates for three separate purposes: developing estimates for individual small area means, producing 

Daniel Bonnéry, Yang Cheng, Neung Soo Ha, Partha Lahiri

Statistics in Transition New Series , ISSUE 4, 511–522

Article

DEVELOPMENT OF SMALLAREA ESTIMATION IN OFFICIAL STATISTICS

. Next, before discussing general issues of small area estimation (SAE) in official statistics, the author reminds: the methods of sampling surveys, data collection, estimation procedures, and data quality assessment used for official statistics. Statistical information is published in different breakdowns with stable or even decreasing budget while being legally bound to control the response burden. Special attention is paid, from a practitioner point of view, to synthetic development of small area

Jan Kordos

Statistics in Transition New Series , ISSUE 1, 105–132

Research Article

MET AND UNMET NEED FOR CONTRACEPTION: SMALL AREA ESTIMATION FOR RAJASTHAN STATE OF INDIA

of unwanted births ends in childbirths, and which are related to deaths and injuries for both mother and child. Due to lack of availability of reliable data at the small level (area-wise) specifically in developing countries like India. In this article the small area estimation technique is used for the estimation of met and unmet need for contraception for 187 towns of Rajasthan state of India and for empirical analysis. Data is taken from the District Level Household Survey (DLHS): 2002-04 and

Piyush Kant Rai, Sarla Pareek, Hemlata Joshi

Statistics in Transition New Series , ISSUE 2, 329–360

Research paper

BORROWING INFORMATION OVER TIME IN BINOMIAL/LOGIT NORMAL MODELS FOR SMALL AREA ESTIMATION

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

Carolina Franco, William R. Bell

Statistics in Transition New Series , ISSUE 4, 563–584

Article

VARIATIONAL APPROXIMATIONS FOR SELECTING HIERARCHICAL MODELS OF CIRCULAR DATA IN A SMALL AREA ESTIMATION APPLICATION

, within spatio temporal domains subdivided by the mode of fishing. Because many of these domains have small sample sizes, small area estimation methods are developed. Bayesian inference for the circular distributions on the 24-hour clock is conducted, based on a large set of observed daily departure times from another National Marine Fisheries Service study, the Coastal Household Telephone Survey. A novel variational/Laplace approximation to the posterior distribution allows fast comparison of a large

Daniel Hernandez-Stumpfhauser, F. Jay Breidt, Jean D. Opsomer

Statistics in Transition New Series , ISSUE 1, 91–104

Article

Robust estimation of wages in small enterprises:  the application to Poland’s districts

The paper presents an empirical study designed to test a small area estimation method. The aim of the study is to apply a robust version of the Fay-Herriot model to the estimation of average wages in the small business sector. Unlike the classical Fay-Herriot model, its robust version makes it possible to meet the assumption of normality of random effects under the presence of outliers. Moreover, the use of this version of the Fay-Herriot model helps to improve the precision of estimates

Grażyna Dehnel, Łukasz Wawrowski

Statistics in Transition New Series , ISSUE 1, 137–157

Article

ESTIMATION OF SMALL AREA CHARACTERISTICS USING MULTIVARIATE RAO-YU MODEL

Alina Jędrzejczak, Jan Kubacki

Statistics in Transition New Series , ISSUE 4, 725–742

Article

Effective transformation-based variable selection under two-fold subarea models in small area estimation

Song Cai, J. N. K. Rao, Laura Dumitrescu, Golshid Chatrchi

Statistics in Transition New Series , ISSUE 4, 68–83

Research paper

POLICY-ORIENTED INFERENCE AND THE ANALYST-CLIENT COOPERATION. AN EXAMPLE FROM SMALL-AREA STATISTICS

Nicholas T. Longford

Statistics in Transition New Series , ISSUE 1, 65–82

Article

ESTIMATION OF INCOME CHARACTERISTICS FOR REGIONS IN POLAND USING SPATIO-TEMPORAL SMALL AREA MODELS

Alina Jędrzejczak, Jan Kubacki

Statistics in Transition New Series , ISSUE 4, 113–134

Article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

Julie Gershunskaya

Statistics in Transition New Series , ISSUE 4, 23–29

Article

Discussion of “Small Area Estimation: Its Evolution in Five Decades”, by Malay Ghosh

J. N. K. Rao

Statistics in Transition New Series , ISSUE 4, 53–58

Article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

Danny Pfeffermann

Statistics in Transition New Series , ISSUE 4, 51–52

Article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

David Newhouse

Statistics in Transition New Series , ISSUE 4, 45–50

Article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

Isabel Molina

Statistics in Transition New Series , ISSUE 4, 40–44

Article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

Ying Han

Statistics in Transition New Series , ISSUE 4, 30–34

Article

Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh

Yan Li

Statistics in Transition New Series , ISSUE 4, 35–39

Article

A TWO-COMPONENT NORMAL MIXTURE ALTERNATIVE TO THE FAY-HERRIOT MODEL

Adrijo Chakraborty, Gauri Sankar Datta, Abhyuday Mandal

Statistics in Transition New Series , ISSUE 1, 67–90

Research Article

On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction

Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require

Adam Chwila, Tomasz Żądło

Statistics in Transition New Series , ISSUE 2, 35–60

Article

Skew normal small area time models for the Brazilian annual service sector survey

produce estimates with acceptable precision for service activities in the North, Northeast and Midwest regions of the country. Therefore, the use of small area estimation models may provide acceptable precise estimates, especially if they take into account temporal dynamics and sector similarity. Besides, skew normal models can handle business data with asymmetric distribution and the presence of outliers. We propose models with domain and time random effects on the intercept and slope. The results

André Felipe Azevedo Neves, Denise Britz do Nascimento Silva, Fernando Antônio da Silva Moura

Statistics in Transition New Series , ISSUE 4, 84–102

Article

MAPPING POVERTY AT THE LEVEL OF SUBREGIONS IN POLAND USING INDIRECT ESTIMATION

estimation methods, which rely on information from outside the subpopulation of interest, which usually increases estimation precision. The main aim of this paper is to show results of estimation of the poverty indicator at a lower level of spatial aggregation than the one used so far, that is at the level of subregions in Poland (NUTS 3) using the small area estimation methodology (SAE), i.e. a model–based technique – the EBLUP estimator based on the Fay–Herriot model. By optimally

Marcin Szymkowiak, Andrzej Młodak, Łukasz Wawrowski

Statistics in Transition New Series , ISSUE 4, 609–635

Article

A Bayesian Small Area Model with Dirichlet Processes on the Responses

Typically survey data have responses with gaps, outliers and ties, and the distributions of the responses might be skewed. Usually, in small area estimation, predictive inference is done using a two-stage Bayesian model with normality at both levels (responses and area means). This is the Scott-Smith (S-S) model and it may not be robust against these features. Another model that can be used to provide a more robust structure is the two-stage Dirichlet process mixture (DPM) model, which has

Jiani Yin, Balgobin Nandram

Statistics in Transition New Series , ISSUE 3, 1–19

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