In this excellent overview of the history of probability and nonprobability sampling from the end... more In this excellent overview of the history of probability and nonprobability sampling from the end of the nineteenth century to the present day, Professor Graham Kalton outlines the essence of past endeavors that helped to define philosophical approaches and stimulate the development of survey sampling methodologies. From the beginning, there was an understanding that a sample should, in some ways, resemble the population under study. In Kiær’s ideas of “representative sampling” and Neyman’s invention of probability-based approach, the prime concern of survey sampling has been to properly plan for representing characteristics of the finite population. Poststratification and other calibration methods were developed for the same important goal of better representation.
Journal of Survey Statistics and Methodology, 2022
Government statistical agencies compose a population statistic for a given domain using a sample ... more Government statistical agencies compose a population statistic for a given domain using a sample of units nested in that domain. Subsequent modeling of these domain survey estimates is often used to “borrow strength” across a dependence structure among the domains to improve estimation accuracy and efficiency. This paper focuses on models jointly defined for sample-based point estimates along with their sample-based estimates of variances. Bias may be present in the sample-based (observed) variances due to small sample sizes or the estimation procedure. We propose a new formulation that extends existing joint model formulations to allow for a multiplicative bias in observed variances. Our approach capitalizes on the unbiasedness property of point estimates. We utilize a nonparametric mixture construction that allows the data to discover distinct bias regimes. As a consequence of the better variance estimation, domain point estimates are more robustly estimated under a joint model fo...
Different methods have been proposed in the small area estimation literature to deal with outlier... more Different methods have been proposed in the small area estimation literature to deal with outliers in individual observations and in the area-level random effects. In this paper, we propose a new method based on a scale mixture of two normal distributions. Using a simulation study, we compare the performance of a few recently proposed robust small area estimators and our proposed estimator based on a mixture distribution. We then compare the proposed method with the existing methods to estimate monthly employment changes in the metropolitan statistical areas using data from the Current Employment Statistics Survey conducted by the U.S. Bureau of Labor Statistics (BLS).
The sampling weight in the Current Employment Statistics Survey is determined at the time of samp... more The sampling weight in the Current Employment Statistics Survey is determined at the time of sample selection. It depends on a unit’s State, industry, and size class. However, the population of businesses is highly dynamic. Establishments constantly grow or contract; sometimes they also change their industrial classification or geographical location. Even the number of population units is not fixed but continuously changes over time. A unit may change its size class at the time of estimation or the content of the original stratum may change. Under such circumstances, application of the original survey weights may increase volatility of survey estimates. In this paper we investigate if the survey estimates can be improved by adjusting the original weights.
In this excellent overview of the history of probability and nonprobability sampling from the end... more In this excellent overview of the history of probability and nonprobability sampling from the end of the nineteenth century to the present day, Professor Graham Kalton outlines the essence of past endeavors that helped to define philosophical approaches and stimulate the development of survey sampling methodologies. From the beginning, there was an understanding that a sample should, in some ways, resemble the population under study. In Kiær’s ideas of “representative sampling” and Neyman’s invention of probability-based approach, the prime concern of survey sampling has been to properly plan for representing characteristics of the finite population. Poststratification and other calibration methods were developed for the same important goal of better representation.
Journal of Survey Statistics and Methodology, 2022
Government statistical agencies compose a population statistic for a given domain using a sample ... more Government statistical agencies compose a population statistic for a given domain using a sample of units nested in that domain. Subsequent modeling of these domain survey estimates is often used to “borrow strength” across a dependence structure among the domains to improve estimation accuracy and efficiency. This paper focuses on models jointly defined for sample-based point estimates along with their sample-based estimates of variances. Bias may be present in the sample-based (observed) variances due to small sample sizes or the estimation procedure. We propose a new formulation that extends existing joint model formulations to allow for a multiplicative bias in observed variances. Our approach capitalizes on the unbiasedness property of point estimates. We utilize a nonparametric mixture construction that allows the data to discover distinct bias regimes. As a consequence of the better variance estimation, domain point estimates are more robustly estimated under a joint model fo...
Different methods have been proposed in the small area estimation literature to deal with outlier... more Different methods have been proposed in the small area estimation literature to deal with outliers in individual observations and in the area-level random effects. In this paper, we propose a new method based on a scale mixture of two normal distributions. Using a simulation study, we compare the performance of a few recently proposed robust small area estimators and our proposed estimator based on a mixture distribution. We then compare the proposed method with the existing methods to estimate monthly employment changes in the metropolitan statistical areas using data from the Current Employment Statistics Survey conducted by the U.S. Bureau of Labor Statistics (BLS).
The sampling weight in the Current Employment Statistics Survey is determined at the time of samp... more The sampling weight in the Current Employment Statistics Survey is determined at the time of sample selection. It depends on a unit’s State, industry, and size class. However, the population of businesses is highly dynamic. Establishments constantly grow or contract; sometimes they also change their industrial classification or geographical location. Even the number of population units is not fixed but continuously changes over time. A unit may change its size class at the time of estimation or the content of the original stratum may change. Under such circumstances, application of the original survey weights may increase volatility of survey estimates. In this paper we investigate if the survey estimates can be improved by adjusting the original weights.
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Papers by Julie Gershunskaya