1,721,004 research outputs found
Invited discussion of paper by J. Jiang and P. Lahiri: Mixed Model Prediction and Small Area Estimation
No full textA parametric empirical likelihood approach to data matching under nonignorable sampling and nonresponse
No full textStatistical matching attempts to combine the information obtained from different non-overlapping samples. The samples selected are often non representative of the finite population from which they are taken and not all the sampled units respond. The aim of this paper is to illustrate how informative sampling and not missing at random (NMAR) nonresponse can be handled in the statistical matching context
Two-stage benchmarking of time-series models fitted to survey estimates for small area estimation
No full text
Robustness considerations in the choice of a method of inference for regression analysis of survey data
No full textA nonparametric approach for statistical matching under informative sampling and nonresponse
No full textStatistical matching attempts to combine the information obtained from different,
non-overlapping samples, selected from the same target population, to form a matched sample
containing the data in the different samples. The aim of this paper is to propose a
nonparametric approach of handling statistical matching under informative sampling and not
missing at random (NMAR) nonresponse, by use of empirical likelihood
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)
Matching Information from Two Independent Informative Samples
No full textStatistical matching deals with the problem of how to combine information
collected in dierent samples taken from the same target population, but on partly
dierent survey variables. The purpose of this paper is to analyze the statistical
matching problem under informative sampling designs, when applying the sample likelihood
approach. First, a conditional independence assumption is made, which allows to dene an
identiable population model under which the conditions guaranteeing the identiability and
estimability of the sample likelihood are investigated. Next, the uncertainty in statistical
matching under informative sampling designs is discussed, with particular attention to the
three-variate normal case. A simulation experiment illustrating the theoretical results is
performed
Accounting for non-ignorable sampling and non-response in statistical matching
Full text linkData for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and are subject to not missing at random non-response. The problem with ignoring the sampling process and non-response is that the distribution of the data observed for the responding units can be very different from the distribution holding for the population data, which may distort the inference process and result in a matched database that misrepresents the joint distribution in the population. Our proposed methodology employs the empirical likelihood approach and is shown to perform well in a simulation experiment and when applied to real sample data
Weighting for unequal selection probabilities in multilevel models
No full textWhen multilevel models are estimated from survey data derived using multistage sampling, unequal selection probabilities at any stage of sampling may induce bias in standard estimators, unless the sources of the unequal probabilities are fully controlled for in the covariates. This paper proposes alternative ways of weighting the estimation of a two-level model by using the reciprocals of the selection probabilities at each stage of sampling. Consistent estimators are obtained when both the sample number of level 2 units and the sample number of level 1 units within sampled level 2 units increase. Scaling of the weights is proposed to improve the properties of the estimators and to simplify computation. Variance estimators are also proposed. In a limited simulation study the scaled weighted estimators are found to perform well, although non-negligible bias starts to arise for informative designs when the sample number of level 1 units becomes small. The variance estimators perform extremely well. The procedures are illustrated using data from the survey of psychiatric morbidity
- …
