Calcutta Statistical Association Bulletin, Ahead of Print.
Chambers and Dunstan proposed a model-based predictor of the population distribution function that makes use of auxiliary population information under a general sampling design. Subsequently, Rao, Kovar, and Mantel proposed design-based ratio and difference predictors of the population distribution function that also use this auxiliary information. Both predictors (CD and RKM) assume a single level model for the target population. In this article we develop predictors of the finite population distribution function for a population that follows a multilevel model. These new predictors use the same smearing approach underpinning the CD predictor. We compare our new predictors with the CD and RKM predictors via design-based simulation, and show that they perform better than these single level predictors when there is significant intra-cluster correlation. The performances of these new two level predictors are also examined via an empirical study based on data from a large-scale UK business survey aimed at estimating the distribution of hourly pay rates.AMS subject classification: Primary 62G30, Secondary 62G32
Category Archives: Calcutta Statistical Association Bulletin
Global-Local Priors for Spatial Small Area Estimation
Calcutta Statistical Association Bulletin, Ahead of Print.
Small area estimation is gaining increasing popularity among survey statisticians. Since the direct estimates of small areas usually have large standard errors, model-based approaches are often adopted to borrow strength across areas. The models often use covariates to link different areas and random effects to account for the additional variation. In the classic Fay-Herriot model, the random effects are assumed to have independent normal distributions with a shared variance. Recent studies showed that random effects are not necessary for all areas, so global-local priors have been introduced in Tang et al.[] to effectively characterize the sparsity in random effects. This article introduces global-local priors in the context of small area estimation where the area level random effects exhibit a spatial structure. This generalizes the findings of Tang et al.[] where independence of the area level effects is assumed. Our findings are illustrated via both simulation and real data examples.AMS subject classification: 62D05, 62M30
Small area estimation is gaining increasing popularity among survey statisticians. Since the direct estimates of small areas usually have large standard errors, model-based approaches are often adopted to borrow strength across areas. The models often use covariates to link different areas and random effects to account for the additional variation. In the classic Fay-Herriot model, the random effects are assumed to have independent normal distributions with a shared variance. Recent studies showed that random effects are not necessary for all areas, so global-local priors have been introduced in Tang et al.[] to effectively characterize the sparsity in random effects. This article introduces global-local priors in the context of small area estimation where the area level random effects exhibit a spatial structure. This generalizes the findings of Tang et al.[] where independence of the area level effects is assumed. Our findings are illustrated via both simulation and real data examples.AMS subject classification: 62D05, 62M30
Preface to the Section on Voluntary Surveys
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 7-7, May 2023.
Discussion
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 33-35, May 2023.
Discussion
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 38-42, May 2023.
Stratified Sampling: Some Associated Problems
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 48-59, May 2023.
Stratified sampling has been commonly used in many large-scale surveys. The two inter-related problems of determining strata boundaries where strata have to be developed using some variable(s) whose values can be accessed while designing the survey and of allocating the total sample size among the strata have been studied during a long period, initially without involving mathematical programming and subsequently using concurrent developments in constrained optimization methods and algorithms. The present article provides a critique of some of these studies, especially those dealing with multiple stratification variables and multiple parameters to be estimated.AMS Subject Classification: 62D05
Stratified sampling has been commonly used in many large-scale surveys. The two inter-related problems of determining strata boundaries where strata have to be developed using some variable(s) whose values can be accessed while designing the survey and of allocating the total sample size among the strata have been studied during a long period, initially without involving mathematical programming and subsequently using concurrent developments in constrained optimization methods and algorithms. The present article provides a critique of some of these studies, especially those dealing with multiple stratification variables and multiple parameters to be estimated.AMS Subject Classification: 62D05
Discussion
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 36-37, May 2023.
Discussion
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 28-32, May 2023.
Rejoinder
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 43-47, May 2023.
An Empirical Likelihood Approach to Reduce Selection Bias in Voluntary Samples
Calcutta Statistical Association Bulletin, Volume 75, Issue 1, Page 8-27, May 2023.
How to construct the pseudo-weights in voluntary samples is an important practical problem in survey sampling. The problem is quite challenging when the sampling mechanism for the voluntary sample is allowed to be non-ignorable. Under the assumption that the sample participation model is correctly specified, we can compute a consistent estimator of the model parameter and construct the propensity score estimator of the population mean. We propose using the empirical likelihood method to construct the final weights for voluntary samples by incorporating the bias calibration constraints and the benchmarking constraints. Linearization variance estimation of the proposed method is developed. A toy example is also presented to illustrate the idea and the computational details. A limited simulation study is also performed to evaluate the performance of the proposed methods.AMS subject classification: 62D10, 63D05
How to construct the pseudo-weights in voluntary samples is an important practical problem in survey sampling. The problem is quite challenging when the sampling mechanism for the voluntary sample is allowed to be non-ignorable. Under the assumption that the sample participation model is correctly specified, we can compute a consistent estimator of the model parameter and construct the propensity score estimator of the population mean. We propose using the empirical likelihood method to construct the final weights for voluntary samples by incorporating the bias calibration constraints and the benchmarking constraints. Linearization variance estimation of the proposed method is developed. A toy example is also presented to illustrate the idea and the computational details. A limited simulation study is also performed to evaluate the performance of the proposed methods.AMS subject classification: 62D10, 63D05