155 research outputs found
Prediction of finite population totals based on the sample distribution
No full textThis article studies the use of the sample distribution for the prediction of finite population totals under single-stage sampling. The proposed predictors employ the sample values of the target study variable, the sampling weights of the sample units and possibly known population values of auxiliary variables. The prediction problem is solved by estimating the expectation of the study values for units outside the sample as a function of the corresponding expectation under the sample distribution and the sampling weights. The prediction mean square error is estimated by a combination of an inverse sampling procedure and a re-sampling method. An interesting outcome of the present analysis is that several familiar estimators in common use are shown to be special cases of the proposed approach, thus providing them a new interpretation. The performance of the new and some old predictors in common use is evaluated and compared by a Monte Carlo simulation study using a real data set
Methodological Issues and Challenges in the Production of Official Statistics: 24th Annual Morris Hansen Lecture
No full textIncluding: comments by Brown, comments by Eltinge, and Rejoinder to Reviewers’ Discussion by Pfeffermann
Small area estimation: new developments and directions
No full textThe purpose of this paper is to provide a critical review of the main advances in small area estimation (SAE) methods in recent years. We also discuss some of the earlier developments, which serve as a necessary background for the new studies. The review focuses on model dependent methods with special emphasis on point prediction of the target area quantities, and mean square error assessments. The new models considered are models used for discrete measurements, time series models and models that arise under informative sampling. The possible gains from modeling the correlations among small area random effects used to represent the unexplained variation of the small area target quantities are examined. For review and appraisal of the earlier methods used for SAE, see Ghosh and Rao (1994)
State-space modeling with correlated measurements with application to small area estimation under benchmark constraints
No full textThe problem of Small Area Estimation is how to produce reliable estimates of area (domain) characteristics, when the sizes within the areas are too small to warrant the use of traditional direct survey estimates. This problem is commonly tackled by borrowing information from either neighboring areas and/or from previous surveys, using appropriate time series/cross-sectional models. In order to protect against possible model breakdowns and for other reasons, it is often required to benchmark the model dependent estimates to the corresponding direct survey estimates in larger areas, for which the survey estimates are sufficiently accurate. The benchmarking process defines another way of borrowing information across the areas.This article shows how benchmarking can be implemented with the state-space models used by the Bureau of Labor Statistics in the U.S. for the production of the monthly employment and unemployment estimates at the state level. The computation of valid estimators for the variances of the benchmarked estimators requires joint modeling of the direct estimators in several states, which in turn requires the development of a filtering algorithm for state-space models with correlated measurement errors. No such algorithm has been developed so far. The application of the proposed procedure is illustrated using real unemployment series
Imputation for wave nonresponse: existing methods and a time series approach
No full textPART I. PERSPECTIVES ON NONRESPONSE.Survey Nonresponse in Design, Data Collection, and Analysis (D. Dillman, et al.).Developing Nonresponse Standards (T. Smith).Trends in Household Survey Nonresponse: A Longitudinal and International Comparison (E. de Leeuw and W. de Heer).Culture and Survey Nonresponse (T. Johnson, et al.).To Answer or Not to Answer: Decision Processes Related to Survey Item Nonresponse (P. Beatty and D. Herrmann).The Causes of No-Opinion Response to Attitude Measures in Surveys: They Are Rarely What They Appear to Be (J. Krosnick).PART II: IMPACTS OF SURVEY DESIGN ON NONRESPONSE.The Influence of Interviewers' Attitude and Behavior on Household Survey Nonresponse: An International Comparison (J. Hox and E. de Leeuw).Persuading Reluctant Recipients in Telephone Surveys (W. Dijkstra and J. Smit).The Effects of Extended Interviewer Efforts on Nonresponse Bias (P. Lynn, et al.).Effect of Item Nonresponse on Nonresponse Error and Inference (R. Mason, et al.).The Use of Incentives to Reduce Nonresponse in Household Surveys (E. Singer).The Influence of Alternative Visual Designs on Respondents' Performance with Branching Instructions in Self-Administered Questionnaires (C. Redline and D. Dillman).PART III: NONRESPONSE IN DIVERSE TYPES OF SURVEYS.Evaluating Nonresponse Error in Mail Surveys (D. Moore and J. Tarnai).Understanding Unit and Item Nonresponse in Business Surveys (D. Willimack, et al.).Nonresponse in Web Surveys (V. Vehovar, et al.).Nonresponse in Exit Polls: A Comprehensive Analysis (D. Merkle and M. Edelman).Nonresponse in the Second Wave of Longitudinal Household Surveys (J. Lepkowski and M. Couper). PART IV: STATISTICAL INFERENCE ACCOUNTING FOR NONRESPONSE.Weighting Nonresponse Adjustments Based on Auxiliary Information (J. Bethlehem).Poststratification and Weighting Adjustments (A. Gelman and J. Carlin).Replication Methods for Variance Estimation in Complex Surveys with Imputed Data (J. Shao).Variance Estimation from Survey Data under Single Imputation (H. Lee, et al.).Large-Scale Imputation for Complex Surveys (D. Marker, et al.).A Congenial Overview and Investigation of Multiple Imputation Inferences under Uncongeniality (X. Meng).Multivariate Imputation of Coarsened Survey Data on Household Wealth (S. Heeringa, et al.).Modeling Nonignorable Attrition and Measurement Error in Panel Surveys: An Application to Travel Demand Modeling (D. Brownstone, et al.).Using Matched Substitutes to Adjust for Nonignorable Nonresponse through Multiple Imputations (D. Rubin and E. Zanutto).Using Administrative Records to Impute for Nonresponse (E. Zanutto and A. Zaslavsky).Imputation for Wave Nonresponse: Existing Methods and a Time Series Approach (D. Pfeffermann and G. Nathan).Diagnostics for the Practical Effects of Nonresponse Adjustment Methods (J. Eltinge)
New important developments in small area estimation
No full textThe purpose of this paper is to review and discuss some of the new important developments in small area estimation (SAE) methods. Rao (2003) wrote a very comprehensive book, which covers all the main developments in this topic until that time and so the focus of this review is on new developments in the last 7 years. However, to make the review more self contained, I also repeat shortly some of the older developments. The review covers both design based and model-dependent methods with emphasis on the prediction of the area target quantities and the assessment of the prediction error. The style of the paper is similar to the style of my previous review on SAE published in 2002, explaining the new problems investigated and describing the proposed solutions, but without dwelling on theoretical details, which can be found in the original articles. I am hoping that this paper will be useful both to researchers who like to learn more on the research carried out in SAE and to practitioners who might be interested in the application of the new methods
Small area estimation under informative probability sampling of areas and within the selected areas
No full textIn this article we show how to predict small area means and obtain valid MSE estimators and confidence intervals when the areas represented in the sample are sampled with unequal probabilities that are possibly related to the true (unknown) area means, and the sampling of units within the selected areas is with probabilities that are possibly related to the outcome values. Ignoring the effects of the sampling process on the distribution of the observed outcomes in such cases may bias the inference very severely. Classical design based inference that uses the randomization distribution of probability weighted estimators cannot be applied for predicting the means of nonsampled areas. We propose simple test statistics for testing the informativeness of the selection of the areas and the sampling of units within the selected areas. The proposed procedures are illustrated by a simulation study and a real application of estimating mean body mass index in counties of the U.S.A, using data from the NHANES III survey
Small-area estimation with state-space models subject to benchmark constraints
No full textThis article shows how to benchmark small area estimators, produced by fitting separate state-space models within the areas, to aggregates of the survey direct estimators within a group of areas. State-space models are used by the U.S. Bureau of Labor Statistics (BLS) for the production of the monthly Employment and Unemployment State estimates. The computation of the benchmarked estimators and their variances is accomplished by incorporating the benchmark constraints within a joint model of the direct estimators in the different areas, which requires the development of a new filtering algorithm for state-space models with correlated measurement errors. No such algorithm has been developed before. The properties and implications of the use of the benchmarked estimators are discussed and illustrated using BLS unemployment series.
The problem of Small Area Estimation is how to produce reliable estimates of area (domain) characteristics, when the sample sizes within the areas are too small to warrant the use of traditional direct survey estimates. This problem is commonly handled by borrowing strength from either neighboring areas and/or from previous surveys, using appropriate cross-sectional/time series models. In order to protect against possible model breakdowns and for consistency in publication, it is often required to benchmark the area model dependent estimates to the direct survey estimate in a group of areas for which the survey estimate is sufficiently accurate. The latter estimate is a weighted sum of the direct estimates in the areas included in the group, so that the benchmarking process defines another way of borrowing strength across the areas
Estimation of treatment effects in observational studies by recovering the assignment probabilities and the population model
No full textIn observational studies the assignment of units to treatments is with unknown probabilities. Consequently, estimation and comparison of treatment effects based on the empirical distributions of the response under the various treatments can be biased since units exposed to one treatment could differ in important but unknown characteristics from units exposed to other treatments. In this article we study the plausibility of analyzing observational data by deriving the parametric distribution of the observed response under a given treatment as a function of the distribution that would be obtained under a strongly ignorable assignment, and the assignment process, which is modeled as a function of the observed data (the response and covariate values). The use of this approach is founded by showing that the sample distribution of the observed responses is identifiable under some general conditions. The goodness of fit of this distribution can be tested by using standard test statistics since it refers to the observed data, but we also develop a new test. The proposed approach allows also testing the assumptions underlying the use of methods that employ instrumental variables, or methods that use propensity scores with a given set of covariates.We assess the performance of the proposed approach and compare it to existing approaches using data collected in the year 2000 by OECD for the Programme for International Student Assessment (PISA). In the present application we compare students’ scores in mathematics between public and private schools in Ireland and conclude, somewhat surprisingly, that the public schools perform better than the private schools. This finding is supported by one of the existing methods as well
Imputation and estimation under nonignorable nonresponse for household surveys with missing covariate information
No full textIn this paper we develop and apply new methods for handling not missing at random (NMAR) nonresponse. We assume a model for the outcome variable under complete response and a model for the response probability, which is allowed to depend on the outcome and auxiliary variables. The two models define the model holding for the outcomes observed for the responding units, which can be tested. Our methods utilize information on the population totals of some or all of the auxiliary variables in the two models, but we do not require that the auxiliary variables are observed for the nonresponding units. We develop an algorithm for estimating the parameters governing the two models and show how to estimate the distributions of the missing covariates and outcomes, which are then used for imputing the missing values for the nonresponding units and for estimating population means and the variances of the estimators. We also consider several test statistics for testing the model fitted to the observed data and study their performance, thus validating the proposed procedure. The new developments are illustrated using simulated data and a real data set collected as part of the Household Expenditure Survey carried out by the Israel Central Bureau of Statistics in 2005
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