Weighting and estimation

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All (14) (0 to 10 of 14 results)

  • Articles and reports: 12-001-X20010015852
    Description:

    We consider the regression composite estimation introduced by Singh (1994, 1996; termed earlier as "modified regression composite" estimation), a version of which (suggested by Fuller 1999) has been implemented for the Canadian Labour Force Survey (CLFS) beginning in January 2000. The regression composite (rc) estimator enhances the generalized regression (gr) estimator used earlier for the CLFS and the well known Gurney-Daly ak-composite estimator in several ways.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015853
    Description:

    The Canadian Labour Force Survey is a monthly survey of households selected according to a stratified multistage design. The sample of households is divided into six panels (rotation groups). A panel remains in the sample for six consecutive months and is then dropped from the sample. In the past, a generalized regression estimator, based only on the current month's data, has been implemented with a regression weights program. In this paper, we study regression composite estimation procedures that make use of sample information from previous periods and that can be implemented with a regression weights program.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015854
    Description:

    This paper looks at a range of estimators applicable to a regularly repeated household survey with controlled overlap between successive surveys. The paper shows how the Best Linear Unbiased Estimator (BLUE) based on a fixed window of time points can be improved by applying the technique of generalised regression. This improved estimator is compared to the AK estimator of Gurney and Daly (1965) and the modified regression estimator of Singh, Kennedy, Wu and Brisebois (1997), using data from the Australian Labour Force Survey.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015855
    Description:

    The Canadian Labour Force Survey (LFS) is a monthly survey with a complex rotating panel design. After extensive studies, including the investigation of a number of alternative methods for exploiting the sample overlap to improve the quality of estimates, the LFS has chosen a composite estimation method which achieves this goal while satisfying practical constraints. In addition, for variables where there is a substantial gain in efficiency, the new time series tend to make more sense from a subject-matter perspective. This makes it easier to explain LFS estimates to users and the media. Because of the reduced variance under composite estimation, for some variables it is now possible to publish monthly estimates where only three-month moving averages were published in the past. In addition, a greater number of series can be successfully seasonally adjusted.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015856
    Description:

    Imputation is commonly used to compensate for item nonresponse. Variance estimation after imputation has generated considerable discussion and several variance estimators have been proposed. We propose a variance estimator based on a pseudo data set used only for variance estimation. Standard complete data variance estimators applied to the pseudo data set lead to consistent estimators for linear estimators under various imputation methods, including without-replacement hot deck imputation and with-replacement hot deck imputation. The asymptotic equivalence of the proposed method and the adjusted jackknife method of Rao and Sitter (1995) is illustrated. The proposed method is directly applicable to variance estimation for two-phase sampling.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015858
    Description:

    The objective of this paper is to study and measure the change (from the initial to the final weight) which results from the procedure used to modify weights. A breakdown of the final weights is proposed in order to evaluate the relative impact of the nonresponse adjustment, the correction for poststratification and the interaction between these two adjustments. This measure of change is used as a tool for comparing the effectiveness of the various methods for adjusting for nonresponse, in particular the methods relying on the formation of Response Homogeneity Groups. The measure of change is examined through a simulation study, which uses data from a Statistics Canada longitudinal survey, the Survey of Labour and Income Dynamics. The measure of change is also applied to data obtained from a second longitudinal survey, the National Longitudinal Survey of Children and Youth.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015859
    Description:

    In 2001, the INSEE conducted a survey to better understand the homeless population. Since there was no survey frame to allow direct access to homeless persons, the survey principle involved sampling the services they received and questioning the individuals who used those services. Weighting the individual input to the survey proved difficult because a single individual could receive several services within the designated reference period. This article shows how it is possible to apply the weight sharing method to resolve this problem. In this type of survey, a single variable can produce several parameters of interest corresponding to populations varying with time. A set of weights corresponds to each definition of parameters. The article focuses, in particular, on "an average day" and "an average week" weight calculation. Information is also provided on the use data to be collected and the nonresponse adjustment.

    Release date: 2001-08-22

  • Surveys and statistical programs – Documentation: 71F0031X2000001
    Description:

    This paper introduces and explains modifications made to the Labour Force Survey estimates in January 2000. Some of these modifications include the adjustment of all LFS estimates to reflect population counts based on the 1996 Census plus the implementation of a new estimation methodology called composite estimation. This new method results in more efficient estimates of month to month change, while improving the quality of monthly level estimates.

    Release date: 2001-06-29

  • Articles and reports: 12-001-X20000025531
    Description:

    Information from list and area sampling frames is combined to obtain efficient estimates of population size and totals. We consider the case where the probabilities of inclusion on the list frames are heterogeneous and are modeled as a function of covariates. We adapt and modify the methodology of Huggins (1989) and Albo (1990) for modeling auxiliary variables in capture-recapture studies using a logistic regression model. We present the results from a simulation study which compares various estimators of frame size and population totals using the logistic regression approach to modeling heterogeneous inclusion probabilities.

    Release date: 2001-02-28

  • Articles and reports: 12-001-X20000025532
    Description:

    When a survey response mechanism depends on a variable of interest measured within the same survey and observed for only part of the sample, the situation is one of nonignorable nonresponse. In such a situation, ignoring the nonresponse can generate significant bias in the estimation of a mean or of a total. To solve this problem, one option is the joint modeling of the response mechanism and the variable of interest, followed by estimation using the maximum likelihood method. The main criticism levelled at this method is that estimation using the maximum likelihood method is based on the hypothesis of error normality for the model involving the variable of interest, and this hypothesis is difficult to verify. In this paper, the author proposes an estimation method that is robust to the hypothesis of normality, so constructed that there is no need to specify the distribution of errors. The method is evaluated using Monte Carlo simulations. The author also proposes a simple method of verifying the validity of the hypothesis of error normality whenever nonresponse is not ignorable.

    Release date: 2001-02-28
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  • Articles and reports: 12-001-X20010015852
    Description:

    We consider the regression composite estimation introduced by Singh (1994, 1996; termed earlier as "modified regression composite" estimation), a version of which (suggested by Fuller 1999) has been implemented for the Canadian Labour Force Survey (CLFS) beginning in January 2000. The regression composite (rc) estimator enhances the generalized regression (gr) estimator used earlier for the CLFS and the well known Gurney-Daly ak-composite estimator in several ways.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015853
    Description:

    The Canadian Labour Force Survey is a monthly survey of households selected according to a stratified multistage design. The sample of households is divided into six panels (rotation groups). A panel remains in the sample for six consecutive months and is then dropped from the sample. In the past, a generalized regression estimator, based only on the current month's data, has been implemented with a regression weights program. In this paper, we study regression composite estimation procedures that make use of sample information from previous periods and that can be implemented with a regression weights program.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015854
    Description:

    This paper looks at a range of estimators applicable to a regularly repeated household survey with controlled overlap between successive surveys. The paper shows how the Best Linear Unbiased Estimator (BLUE) based on a fixed window of time points can be improved by applying the technique of generalised regression. This improved estimator is compared to the AK estimator of Gurney and Daly (1965) and the modified regression estimator of Singh, Kennedy, Wu and Brisebois (1997), using data from the Australian Labour Force Survey.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015855
    Description:

    The Canadian Labour Force Survey (LFS) is a monthly survey with a complex rotating panel design. After extensive studies, including the investigation of a number of alternative methods for exploiting the sample overlap to improve the quality of estimates, the LFS has chosen a composite estimation method which achieves this goal while satisfying practical constraints. In addition, for variables where there is a substantial gain in efficiency, the new time series tend to make more sense from a subject-matter perspective. This makes it easier to explain LFS estimates to users and the media. Because of the reduced variance under composite estimation, for some variables it is now possible to publish monthly estimates where only three-month moving averages were published in the past. In addition, a greater number of series can be successfully seasonally adjusted.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015856
    Description:

    Imputation is commonly used to compensate for item nonresponse. Variance estimation after imputation has generated considerable discussion and several variance estimators have been proposed. We propose a variance estimator based on a pseudo data set used only for variance estimation. Standard complete data variance estimators applied to the pseudo data set lead to consistent estimators for linear estimators under various imputation methods, including without-replacement hot deck imputation and with-replacement hot deck imputation. The asymptotic equivalence of the proposed method and the adjusted jackknife method of Rao and Sitter (1995) is illustrated. The proposed method is directly applicable to variance estimation for two-phase sampling.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015858
    Description:

    The objective of this paper is to study and measure the change (from the initial to the final weight) which results from the procedure used to modify weights. A breakdown of the final weights is proposed in order to evaluate the relative impact of the nonresponse adjustment, the correction for poststratification and the interaction between these two adjustments. This measure of change is used as a tool for comparing the effectiveness of the various methods for adjusting for nonresponse, in particular the methods relying on the formation of Response Homogeneity Groups. The measure of change is examined through a simulation study, which uses data from a Statistics Canada longitudinal survey, the Survey of Labour and Income Dynamics. The measure of change is also applied to data obtained from a second longitudinal survey, the National Longitudinal Survey of Children and Youth.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20010015859
    Description:

    In 2001, the INSEE conducted a survey to better understand the homeless population. Since there was no survey frame to allow direct access to homeless persons, the survey principle involved sampling the services they received and questioning the individuals who used those services. Weighting the individual input to the survey proved difficult because a single individual could receive several services within the designated reference period. This article shows how it is possible to apply the weight sharing method to resolve this problem. In this type of survey, a single variable can produce several parameters of interest corresponding to populations varying with time. A set of weights corresponds to each definition of parameters. The article focuses, in particular, on "an average day" and "an average week" weight calculation. Information is also provided on the use data to be collected and the nonresponse adjustment.

    Release date: 2001-08-22

  • Articles and reports: 12-001-X20000025531
    Description:

    Information from list and area sampling frames is combined to obtain efficient estimates of population size and totals. We consider the case where the probabilities of inclusion on the list frames are heterogeneous and are modeled as a function of covariates. We adapt and modify the methodology of Huggins (1989) and Albo (1990) for modeling auxiliary variables in capture-recapture studies using a logistic regression model. We present the results from a simulation study which compares various estimators of frame size and population totals using the logistic regression approach to modeling heterogeneous inclusion probabilities.

    Release date: 2001-02-28

  • Articles and reports: 12-001-X20000025532
    Description:

    When a survey response mechanism depends on a variable of interest measured within the same survey and observed for only part of the sample, the situation is one of nonignorable nonresponse. In such a situation, ignoring the nonresponse can generate significant bias in the estimation of a mean or of a total. To solve this problem, one option is the joint modeling of the response mechanism and the variable of interest, followed by estimation using the maximum likelihood method. The main criticism levelled at this method is that estimation using the maximum likelihood method is based on the hypothesis of error normality for the model involving the variable of interest, and this hypothesis is difficult to verify. In this paper, the author proposes an estimation method that is robust to the hypothesis of normality, so constructed that there is no need to specify the distribution of errors. The method is evaluated using Monte Carlo simulations. The author also proposes a simple method of verifying the validity of the hypothesis of error normality whenever nonresponse is not ignorable.

    Release date: 2001-02-28

  • Articles and reports: 12-001-X20000025533
    Description:

    It is common practice to estimate the design effect due to weighting by 1 plus the relative variance of the weights in the sample. This formula has been justified when the selection probabilities are uncorrelated with the variable of interest. An approximation to the design effect is provided to accommodate the situation in which correlation is present.

    Release date: 2001-02-28
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Reference (1) ((1 result))

  • Surveys and statistical programs – Documentation: 71F0031X2000001
    Description:

    This paper introduces and explains modifications made to the Labour Force Survey estimates in January 2000. Some of these modifications include the adjustment of all LFS estimates to reflect population counts based on the 1996 Census plus the implementation of a new estimation methodology called composite estimation. This new method results in more efficient estimates of month to month change, while improving the quality of monthly level estimates.

    Release date: 2001-06-29
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