Simulations
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All (5)
All (5) ((5 results))
- 1. The DYSEM Microsimulation Modelling Platform ArchivedArticles and reports: 11-633-X2017008Description:
The DYSEM microsimulation modelling platform provides a demographic and socioeconomic core that can be readily built upon to develop custom dynamic microsimulation models or applications. This paper describes DYSEM and provides an overview of its intended uses, as well as the methods and data used in its development.
Release date: 2017-07-28 - Articles and reports: 82-003-X201501214293Description:
The University of Wisconsin Cancer Intervention and Surveillance Modeling Network breast cancer microsimulation model was adapted to simulate breast cancer incidence and screening performance in Canada. The model considered effects of breast density on the sensitivity and specificity of screening. The model’s ability to predict age-specific incidence of breast cancer was assessed.
Release date: 2015-12-16 - 3. Bias corrections for survey estimates from data with ratio imputed values for confounded nonresponse ArchivedArticles and reports: 12-001-X199400214423Description:
Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.
Release date: 1994-12-15 - Articles and reports: 12-001-X199300114475Description:
In the creation of micro-simulation databases which are frequently used by policy analysts and planners, several datafiles are combined by statistical matching techniques for enriching the host datafile. This process requires the conditional independence assumption (CIA) which could lead to serious bias in the resulting joint relationships among variables. Appropriate auxiliary information could be used to avoid the CIA. In this report, methods of statistical matching corresponding to three methods of imputation, namely, regression, hot deck, and log linear, with and without auxiliary information are considered. The log linear methods consist of adding categorical constraints to either the regression or hot deck methods. Based on an extensive simulation study with synthetic data, sensitivity analyses for departures from the CIA are performed and gains from using auxiliary information are discussed. Different scenarios for the underlying distribution and relationships, such as symmetric versus skewed data and proxy versus nonproxy auxiliary data, are created using synthetic data. Some recommendations on the use of statistical matching methods are also made. Specifically, it was confirmed that the CIA could be a serious limitation which could be overcome by the use of appropriate auxiliary information. Hot deck methods were found to be generally preferable to regression methods. Also, when auxiliary information is available, log linear categorical constraints can improve performance of hot deck methods. This study was motivated by concerns about the use of the CIA in the construction of the Social Policy Simulation Database at Statistics Canada.
Release date: 1993-06-15 - 5. County estimates of wheat production ArchivedArticles and reports: 12-001-X199100214501Description:
Although farm surveys carried out by the USDA are used to estimate crop production at the state and national levels, small area estimates at the county level are more useful for local economic decision making. County estimates are also in demand by companies selling fertilizers, pesticides, crop insurance, and farm equipment. Individual states often conduct their own surveys to provide data for county estimates of farm production. Typically, these state surveys are not carried out using probability sampling methods. An additional complication is that states impose the constraint that the sum of county estimates of crop production for all counties in a state be equal to the USDA estimate for that state. Thus, standard small area estimation procedures are not directly applicable to this problem. In this paper, we consider using regression models for obtaining county estimates of wheat production in Kansas. We describe a simulation study comparing the resulting estimates to those obtained using two standard small area estimators: the synthetic and direct estimators. We also compare several strategies for scaling the initial estimates so that they agree with the USDA estimate of the state production total.
Release date: 1991-12-16
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Analysis (5)
Analysis (5) ((5 results))
- 1. The DYSEM Microsimulation Modelling Platform ArchivedArticles and reports: 11-633-X2017008Description:
The DYSEM microsimulation modelling platform provides a demographic and socioeconomic core that can be readily built upon to develop custom dynamic microsimulation models or applications. This paper describes DYSEM and provides an overview of its intended uses, as well as the methods and data used in its development.
Release date: 2017-07-28 - Articles and reports: 82-003-X201501214293Description:
The University of Wisconsin Cancer Intervention and Surveillance Modeling Network breast cancer microsimulation model was adapted to simulate breast cancer incidence and screening performance in Canada. The model considered effects of breast density on the sensitivity and specificity of screening. The model’s ability to predict age-specific incidence of breast cancer was assessed.
Release date: 2015-12-16 - 3. Bias corrections for survey estimates from data with ratio imputed values for confounded nonresponse ArchivedArticles and reports: 12-001-X199400214423Description:
Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.
Release date: 1994-12-15 - Articles and reports: 12-001-X199300114475Description:
In the creation of micro-simulation databases which are frequently used by policy analysts and planners, several datafiles are combined by statistical matching techniques for enriching the host datafile. This process requires the conditional independence assumption (CIA) which could lead to serious bias in the resulting joint relationships among variables. Appropriate auxiliary information could be used to avoid the CIA. In this report, methods of statistical matching corresponding to three methods of imputation, namely, regression, hot deck, and log linear, with and without auxiliary information are considered. The log linear methods consist of adding categorical constraints to either the regression or hot deck methods. Based on an extensive simulation study with synthetic data, sensitivity analyses for departures from the CIA are performed and gains from using auxiliary information are discussed. Different scenarios for the underlying distribution and relationships, such as symmetric versus skewed data and proxy versus nonproxy auxiliary data, are created using synthetic data. Some recommendations on the use of statistical matching methods are also made. Specifically, it was confirmed that the CIA could be a serious limitation which could be overcome by the use of appropriate auxiliary information. Hot deck methods were found to be generally preferable to regression methods. Also, when auxiliary information is available, log linear categorical constraints can improve performance of hot deck methods. This study was motivated by concerns about the use of the CIA in the construction of the Social Policy Simulation Database at Statistics Canada.
Release date: 1993-06-15 - 5. County estimates of wheat production ArchivedArticles and reports: 12-001-X199100214501Description:
Although farm surveys carried out by the USDA are used to estimate crop production at the state and national levels, small area estimates at the county level are more useful for local economic decision making. County estimates are also in demand by companies selling fertilizers, pesticides, crop insurance, and farm equipment. Individual states often conduct their own surveys to provide data for county estimates of farm production. Typically, these state surveys are not carried out using probability sampling methods. An additional complication is that states impose the constraint that the sum of county estimates of crop production for all counties in a state be equal to the USDA estimate for that state. Thus, standard small area estimation procedures are not directly applicable to this problem. In this paper, we consider using regression models for obtaining county estimates of wheat production in Kansas. We describe a simulation study comparing the resulting estimates to those obtained using two standard small area estimators: the synthetic and direct estimators. We also compare several strategies for scaling the initial estimates so that they agree with the USDA estimate of the state production total.
Release date: 1991-12-16
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