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- Articles and reports: 82-003-X201901100002Description:
The current study sought to describe the psychometric properties of a brief measure of combat exposure among Canadian Armed Forces (CAF) personnel. Data from post-deployment screening were used to examine the utility of the 8-item Combat Experience Scale (CES-8) as a potential alternative to the 30-item scale (CES-30) in the contexts of both screening and survey research.
Release date: 2019-11-20 - Articles and reports: 12-001-X201900200004Description:
Benchmarking lower level estimates to upper level estimates is an important activity at the United States Department of Agriculture’s National Agricultural Statistical Service (NASS) (e.g., benchmarking county estimates to state estimates for corn acreage). Assuming that a county is a small area, we use the original Fay-Herriot model to obtain a general Bayesian method to benchmark county estimates to the state estimate (the target). Here the target is assumed known, and the county estimates are obtained subject to the constraint that these estimates must sum to the target. This is an external benchmarking; it is important for official statistics, not just NASS, and it occurs more generally in small area estimation. One can benchmark these estimates by “deleting” one of the counties (typically the last one) to incorporate the benchmarking constraint into the model. However, it is also true that the estimates may change depending on which county is deleted when the constraint is included in the model. Our current contribution is to give each small area a chance to be deleted, and we call this procedure the random deletion benchmarking method. We show empirically that there are differences in the estimates as to which county is deleted and that there are differences of these estimates from those obtained from random deletion as well. Although these differences may be considered small, it is most sensible to use random deletion because it does not give preferential treatment to any county and it can provide small improvement in precision over deleting the last one benchmarking as well.
Release date: 2019-06-27 - Articles and reports: 12-001-X201900200006Description:
This paper presents a new algorithm to solve the one-dimensional optimal stratification problem, which reduces to just determining stratum boundaries. When the number of strata H and the total sample size n are fixed, the stratum boundaries are obtained by minimizing the variance of the estimator of a total for the stratification variable. This algorithm uses the Biased Random Key Genetic Algorithm (BRKGA) metaheuristic to search for the optimal solution. This metaheuristic has been shown to produce good quality solutions for many optimization problems in modest computing times. The algorithm is implemented in the R package stratbr available from CRAN (de Moura Brito, do Nascimento Silva and da Veiga, 2017a). Numerical results are provided for a set of 27 populations, enabling comparison of the new algorithm with some competing approaches available in the literature. The algorithm outperforms simpler approximation-based approaches as well as a couple of other optimization-based approaches. It also matches the performance of the best available optimization-based approach due to Kozak (2004). Its main advantage over Kozak’s approach is the coupling of the optimal stratification with the optimal allocation proposed by de Moura Brito, do Nascimento Silva, Silva Semaan and Maculan (2015), thus ensuring that if the stratification bounds obtained achieve the global optimal, then the overall solution will be the global optimum for the stratification bounds and sample allocation.
Release date: 2019-06-27 - 4. Bayesian small area demography ArchivedArticles and reports: 12-001-X201900100001Description:
Demographers are facing increasing pressure to disaggregate their estimates and forecasts by characteristics such as region, ethnicity, and income. Traditional demographic methods were designed for large samples, and perform poorly with disaggregated data. Methods based on formal Bayesian statistical models offer better performance. We illustrate with examples from a long-term project to develop Bayesian approaches to demographic estimation and forecasting. In our first example, we estimate mortality rates disaggregated by age and sex for a small population. In our second example, we simultaneously estimate and forecast obesity prevalence disaggregated by age. We conclude by addressing two traditional objections to the use of Bayesian methods in statistical agencies.
Release date: 2019-05-07 - Articles and reports: 82-003-X201900400001Description:
This study, based on the linked Canadian Community Health Survey-Longitudinal Immigration database, offers a first look at the healthy immigrant effect among selected immigrants arriving under the Immigration and Refugee Protection Act by comparing these results with those for their Canadian-born counterparts.
Release date: 2019-04-17
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Articles and reports (5)
Articles and reports (5) ((5 results))
- Articles and reports: 82-003-X201901100002Description:
The current study sought to describe the psychometric properties of a brief measure of combat exposure among Canadian Armed Forces (CAF) personnel. Data from post-deployment screening were used to examine the utility of the 8-item Combat Experience Scale (CES-8) as a potential alternative to the 30-item scale (CES-30) in the contexts of both screening and survey research.
Release date: 2019-11-20 - Articles and reports: 12-001-X201900200004Description:
Benchmarking lower level estimates to upper level estimates is an important activity at the United States Department of Agriculture’s National Agricultural Statistical Service (NASS) (e.g., benchmarking county estimates to state estimates for corn acreage). Assuming that a county is a small area, we use the original Fay-Herriot model to obtain a general Bayesian method to benchmark county estimates to the state estimate (the target). Here the target is assumed known, and the county estimates are obtained subject to the constraint that these estimates must sum to the target. This is an external benchmarking; it is important for official statistics, not just NASS, and it occurs more generally in small area estimation. One can benchmark these estimates by “deleting” one of the counties (typically the last one) to incorporate the benchmarking constraint into the model. However, it is also true that the estimates may change depending on which county is deleted when the constraint is included in the model. Our current contribution is to give each small area a chance to be deleted, and we call this procedure the random deletion benchmarking method. We show empirically that there are differences in the estimates as to which county is deleted and that there are differences of these estimates from those obtained from random deletion as well. Although these differences may be considered small, it is most sensible to use random deletion because it does not give preferential treatment to any county and it can provide small improvement in precision over deleting the last one benchmarking as well.
Release date: 2019-06-27 - Articles and reports: 12-001-X201900200006Description:
This paper presents a new algorithm to solve the one-dimensional optimal stratification problem, which reduces to just determining stratum boundaries. When the number of strata H and the total sample size n are fixed, the stratum boundaries are obtained by minimizing the variance of the estimator of a total for the stratification variable. This algorithm uses the Biased Random Key Genetic Algorithm (BRKGA) metaheuristic to search for the optimal solution. This metaheuristic has been shown to produce good quality solutions for many optimization problems in modest computing times. The algorithm is implemented in the R package stratbr available from CRAN (de Moura Brito, do Nascimento Silva and da Veiga, 2017a). Numerical results are provided for a set of 27 populations, enabling comparison of the new algorithm with some competing approaches available in the literature. The algorithm outperforms simpler approximation-based approaches as well as a couple of other optimization-based approaches. It also matches the performance of the best available optimization-based approach due to Kozak (2004). Its main advantage over Kozak’s approach is the coupling of the optimal stratification with the optimal allocation proposed by de Moura Brito, do Nascimento Silva, Silva Semaan and Maculan (2015), thus ensuring that if the stratification bounds obtained achieve the global optimal, then the overall solution will be the global optimum for the stratification bounds and sample allocation.
Release date: 2019-06-27 - 4. Bayesian small area demography ArchivedArticles and reports: 12-001-X201900100001Description:
Demographers are facing increasing pressure to disaggregate their estimates and forecasts by characteristics such as region, ethnicity, and income. Traditional demographic methods were designed for large samples, and perform poorly with disaggregated data. Methods based on formal Bayesian statistical models offer better performance. We illustrate with examples from a long-term project to develop Bayesian approaches to demographic estimation and forecasting. In our first example, we estimate mortality rates disaggregated by age and sex for a small population. In our second example, we simultaneously estimate and forecast obesity prevalence disaggregated by age. We conclude by addressing two traditional objections to the use of Bayesian methods in statistical agencies.
Release date: 2019-05-07 - Articles and reports: 82-003-X201900400001Description:
This study, based on the linked Canadian Community Health Survey-Longitudinal Immigration database, offers a first look at the healthy immigrant effect among selected immigrants arriving under the Immigration and Refugee Protection Act by comparing these results with those for their Canadian-born counterparts.
Release date: 2019-04-17
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