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- 1. Design-based estimation of small and empty domains in survey data analysis using order constraintsArticles and reports: 12-001-X202400200010Description: Recent work in survey domain estimation has shown that incorporating a priori assumptions about orderings of population domain means reduces the variance of the estimators and provides smaller confidence intervals with good coverage. Here we show how partial ordering assumptions allow design-based estimation of sample means in domains for which the sample size is zero, with conservative variance estimates and confidence intervals. Order restrictions can also substantially improve estimation and inference in small-size domains. Examples with well-known survey data sets demonstrate the utility of the methods. Code to implement the examples using the R package csurvey is given in the appendix.Release date: 2024-12-20
- Articles and reports: 12-001-X202300100001Description: Recent work in survey domain estimation allows for estimation of population domain means under a priori assumptions expressed in terms of linear inequality constraints. For example, it might be known that the population means are non-decreasing along ordered domains. Imposing the constraints has been shown to provide estimators with smaller variance and tighter confidence intervals. In this paper we consider a formal test of the null hypothesis that all the constraints are binding, versus the alternative that at least one constraint is non-binding. The test of constant versus increasing domain means is a special case. The power of the test is substantially better than the test with the same null hypothesis and an unconstrained alternative. The new test is used with data from the National Survey of College Graduates, to show that salaries are positively related to the subject’s father’s educational level, across fields of study and over several years of cohorts.Release date: 2023-06-30
- Articles and reports: 12-001-X202000200002Description:
In many large-scale surveys, estimates are produced for numerous small domains defined by cross-classifications of demographic, geographic and other variables. Even though the overall sample size of such surveys might be very large, samples sizes for domains are sometimes too small for reliable estimation. We propose an improved estimation approach that is applicable when “natural” or qualitative relationships (such as orderings or other inequality constraints) can be formulated for the domain means at the population level. We stay within a design-based inferential framework but impose constraints representing these relationships on the sample-based estimates. The resulting constrained domain estimator is shown to be design consistent and asymptotically normally distributed as long as the constraints are asymptotically satisfied at the population level. The estimator and its associated variance estimator are readily implemented in practice. The applicability of the method is illustrated on data from the 2015 U.S. National Survey of College Graduates.
Release date: 2020-12-15
Articles and reports (3)
Articles and reports (3) ((3 results))
- 1. Design-based estimation of small and empty domains in survey data analysis using order constraintsArticles and reports: 12-001-X202400200010Description: Recent work in survey domain estimation has shown that incorporating a priori assumptions about orderings of population domain means reduces the variance of the estimators and provides smaller confidence intervals with good coverage. Here we show how partial ordering assumptions allow design-based estimation of sample means in domains for which the sample size is zero, with conservative variance estimates and confidence intervals. Order restrictions can also substantially improve estimation and inference in small-size domains. Examples with well-known survey data sets demonstrate the utility of the methods. Code to implement the examples using the R package csurvey is given in the appendix.Release date: 2024-12-20
- Articles and reports: 12-001-X202300100001Description: Recent work in survey domain estimation allows for estimation of population domain means under a priori assumptions expressed in terms of linear inequality constraints. For example, it might be known that the population means are non-decreasing along ordered domains. Imposing the constraints has been shown to provide estimators with smaller variance and tighter confidence intervals. In this paper we consider a formal test of the null hypothesis that all the constraints are binding, versus the alternative that at least one constraint is non-binding. The test of constant versus increasing domain means is a special case. The power of the test is substantially better than the test with the same null hypothesis and an unconstrained alternative. The new test is used with data from the National Survey of College Graduates, to show that salaries are positively related to the subject’s father’s educational level, across fields of study and over several years of cohorts.Release date: 2023-06-30
- Articles and reports: 12-001-X202000200002Description:
In many large-scale surveys, estimates are produced for numerous small domains defined by cross-classifications of demographic, geographic and other variables. Even though the overall sample size of such surveys might be very large, samples sizes for domains are sometimes too small for reliable estimation. We propose an improved estimation approach that is applicable when “natural” or qualitative relationships (such as orderings or other inequality constraints) can be formulated for the domain means at the population level. We stay within a design-based inferential framework but impose constraints representing these relationships on the sample-based estimates. The resulting constrained domain estimator is shown to be design consistent and asymptotically normally distributed as long as the constraints are asymptotically satisfied at the population level. The estimator and its associated variance estimator are readily implemented in practice. The applicability of the method is illustrated on data from the 2015 U.S. National Survey of College Graduates.
Release date: 2020-12-15