Weighting and estimation
Results
All (19)
All (19) (0 to 10 of 19 results)
- Articles and reports: 75F0002M1996007Description:
This study identifies differences between various aggregate, average and other income estimates produced by the 1993 income data from the Survey of Labour and Income Dynamics and the Survey of Consumer Finances. It also quantifies these differences where possible.
Release date: 1997-12-31 - Articles and reports: 91F0015M1997004Geography: CanadaDescription:
The estimation of the population by age, sex and marital status for each province is a difficult task, principally because of migration. The characteristics of migrants are available only from responses to the census. Until 1991, the census included only the question on place of residence five years ago. Thus, a person who had a different residence five years earlier was considered as a migrant and was attributed the characteristics reported for him/her at the time of the census. However, the respondent had up to five years to change characteristics, particularly those relating to marital status.
Since 1991, the census has asked a question on the place of residence one year ago. The same procedure attributes to the migrant the characteristics reported one year earlier, but this time there is only one year to change them.The article describes, in some detail, the methods now used by Statistics Canada to estimate the characteristics of migrants and evaluates the advantages of using the data on place of residence one year ago.
Release date: 1997-12-23 - 3. A computer algebra for sample survey theory ArchivedArticles and reports: 12-001-X19970013100Description:
A system of procedures that can be used to automate complicated algebraic calculations frequently encountered in sample survey theory is introduced. It is shown that three basic techniques in sampling theory depend on the repeated application of rules that give rise to partitions: the computation of expected values under any unistage sampling design, the determination of unbiased or consistent estimators under these designs and the calculation of Taylor series expansions. The methodology is illustrated here through applications to moment calculations of the sample mean, the ratio estimator and the regression estimator under the special case of simply random sampling without replacement. The innovation presented here is that calculations can now be performed instantaneously on a computer without error and without reliance on existing formulae which may be long and involved. One other immediate benefit of this is that calculations can be performed where no formulae which may be long and involved. One other immediate benefit of this is that calculations can be performed where no formulae presently exist. The computer code developed to implement this methodology is available via anonymous ftp at fisher.stats.uwo.ca.
Release date: 1997-08-18 - 4. Inverse sampling design algorithms ArchivedArticles and reports: 12-001-X19970013101Description:
In the main body of statistics, sampling is often disposed of by assuming a sampling process that selects random variables such that they are independent and identically distributed (IID). Important techniques, like regression and contingency table analysis, were developed largely in the IID world; hence, adjustments are needed to use them in complex survey settings. Rather than adjust the analysis, however, what is new in the present formulation is to draw a second sample from the original sample. In this second sample, the first set of selections are inverted, so as to yield at the end a simple random sample. Of course, to employ this two-step process to draw a single simple random sample from the usually much larger complex survey would be inefficient, so multiple simple random samples are drawn and a way to base inferences on them developed. Not all original samples can be inverted; but many practical special cases are discussed which cover a wide range of practices.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013102Description:
The selection of auxiliary variables is considered for regression estimation in finite populations under a simple random sampling design. This problem is a basic one for model-based and model-assisted survey sampling approaches and is of practical importance when the number of variables available is large. An approach is developed in which a mean squared error estimator is minimised. This approach is compared to alternative approaches using a fixed set of auxiliary variables, a conventional significance test criterion, a condition number reduction approach and a ridge regression approach. The proposed approach is found to perform well in terms of efficiency. It is noted that the variable selection approach affects the properties of standard variance estimators and thus leads to a problem of variance estimation.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013103Description:
This paper discusses the use of some simple diagnostics to guide the formation of nonresponse adjustment cells. Following Little (1986), we consider construction of adjustment cells by grouping sample units according to their estimated response probabilities or estimated survey items. Four issues receive principal attention: assessment of the sensitivity of adjusted mean estimates to changes in k, the number of cells used; identification of specific cells that require additional refinement; comparison of adjusted and unadjusted mean estimates; and comparison of estimation results from estimated-probability and estimated-item based cells. The proposed methods are motivated and illustrated with an application involving estimation of mean consumer unit income from the U.S. Consumer Expenditure Survey.
Release date: 1997-08-18 - 7. Variance estimation for measures of income inequality and polarization - An empirical study ArchivedArticles and reports: 12-001-X19970013104Description:
Measures of income inequality and polarization are fundamental to the discussions of many economic and thus their variances are not expressible by simple formulae and one must rely on approximate variance estimation techniques. In this paper, several methods of variance estimation for six particular income inequality and polarization measures are summarized and their performance is investigated empirically through a simulation study based on the Canadian Survey of Consumer Finance. Our findings indicate that for the measures studied here, the bootstrap and the estimating equations approach perform considerably better than the other methods.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013105Description:
The problem of estimating transition rates from longitudinal survey data in the presence of misclassification error is considered. Approaches which use external information on misclassification rates are reviewed, together with alternative models for measurement error. We define categorical instrumental variables and propose methods for the identification and estimation of models including such variables by viewing the model as a restricted latent class model. The numerical properties of the implied instrumental variable estimators of flow rates are studied using data from the Panel Study of Income Dynamics.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013106Description:
The standard error estimation method used for sample data in the U.S. Decennial Census from 1970 through 1990 yielded irregular results. For example, the method gave different standard error estimates for the "yes" and "no" response for the same binomial variable, when both standard error estimates should have been the same. If most respondents answered a binomial variable one way and a few answered the other way, the standard error estimate was much higher for the response with the most respondents. In addition, when 100 percent of respondents answered a question the same way, the standard error of this estimate was not zero, but was still quite high. Reporting average design effects which were weighted by the number of respondents that reported particular characteristics magnified the problem. An alternative to the random groups standard error estimate used in the U.S. census is suggested here.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013108Description:
We show how the use of matrix calculus can simplify the derivation of the linearization of the regression coefficient estimator and the regression estimator.
Release date: 1997-08-18
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Analysis (19)
Analysis (19) (0 to 10 of 19 results)
- Articles and reports: 75F0002M1996007Description:
This study identifies differences between various aggregate, average and other income estimates produced by the 1993 income data from the Survey of Labour and Income Dynamics and the Survey of Consumer Finances. It also quantifies these differences where possible.
Release date: 1997-12-31 - Articles and reports: 91F0015M1997004Geography: CanadaDescription:
The estimation of the population by age, sex and marital status for each province is a difficult task, principally because of migration. The characteristics of migrants are available only from responses to the census. Until 1991, the census included only the question on place of residence five years ago. Thus, a person who had a different residence five years earlier was considered as a migrant and was attributed the characteristics reported for him/her at the time of the census. However, the respondent had up to five years to change characteristics, particularly those relating to marital status.
Since 1991, the census has asked a question on the place of residence one year ago. The same procedure attributes to the migrant the characteristics reported one year earlier, but this time there is only one year to change them.The article describes, in some detail, the methods now used by Statistics Canada to estimate the characteristics of migrants and evaluates the advantages of using the data on place of residence one year ago.
Release date: 1997-12-23 - 3. A computer algebra for sample survey theory ArchivedArticles and reports: 12-001-X19970013100Description:
A system of procedures that can be used to automate complicated algebraic calculations frequently encountered in sample survey theory is introduced. It is shown that three basic techniques in sampling theory depend on the repeated application of rules that give rise to partitions: the computation of expected values under any unistage sampling design, the determination of unbiased or consistent estimators under these designs and the calculation of Taylor series expansions. The methodology is illustrated here through applications to moment calculations of the sample mean, the ratio estimator and the regression estimator under the special case of simply random sampling without replacement. The innovation presented here is that calculations can now be performed instantaneously on a computer without error and without reliance on existing formulae which may be long and involved. One other immediate benefit of this is that calculations can be performed where no formulae which may be long and involved. One other immediate benefit of this is that calculations can be performed where no formulae presently exist. The computer code developed to implement this methodology is available via anonymous ftp at fisher.stats.uwo.ca.
Release date: 1997-08-18 - 4. Inverse sampling design algorithms ArchivedArticles and reports: 12-001-X19970013101Description:
In the main body of statistics, sampling is often disposed of by assuming a sampling process that selects random variables such that they are independent and identically distributed (IID). Important techniques, like regression and contingency table analysis, were developed largely in the IID world; hence, adjustments are needed to use them in complex survey settings. Rather than adjust the analysis, however, what is new in the present formulation is to draw a second sample from the original sample. In this second sample, the first set of selections are inverted, so as to yield at the end a simple random sample. Of course, to employ this two-step process to draw a single simple random sample from the usually much larger complex survey would be inefficient, so multiple simple random samples are drawn and a way to base inferences on them developed. Not all original samples can be inverted; but many practical special cases are discussed which cover a wide range of practices.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013102Description:
The selection of auxiliary variables is considered for regression estimation in finite populations under a simple random sampling design. This problem is a basic one for model-based and model-assisted survey sampling approaches and is of practical importance when the number of variables available is large. An approach is developed in which a mean squared error estimator is minimised. This approach is compared to alternative approaches using a fixed set of auxiliary variables, a conventional significance test criterion, a condition number reduction approach and a ridge regression approach. The proposed approach is found to perform well in terms of efficiency. It is noted that the variable selection approach affects the properties of standard variance estimators and thus leads to a problem of variance estimation.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013103Description:
This paper discusses the use of some simple diagnostics to guide the formation of nonresponse adjustment cells. Following Little (1986), we consider construction of adjustment cells by grouping sample units according to their estimated response probabilities or estimated survey items. Four issues receive principal attention: assessment of the sensitivity of adjusted mean estimates to changes in k, the number of cells used; identification of specific cells that require additional refinement; comparison of adjusted and unadjusted mean estimates; and comparison of estimation results from estimated-probability and estimated-item based cells. The proposed methods are motivated and illustrated with an application involving estimation of mean consumer unit income from the U.S. Consumer Expenditure Survey.
Release date: 1997-08-18 - 7. Variance estimation for measures of income inequality and polarization - An empirical study ArchivedArticles and reports: 12-001-X19970013104Description:
Measures of income inequality and polarization are fundamental to the discussions of many economic and thus their variances are not expressible by simple formulae and one must rely on approximate variance estimation techniques. In this paper, several methods of variance estimation for six particular income inequality and polarization measures are summarized and their performance is investigated empirically through a simulation study based on the Canadian Survey of Consumer Finance. Our findings indicate that for the measures studied here, the bootstrap and the estimating equations approach perform considerably better than the other methods.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013105Description:
The problem of estimating transition rates from longitudinal survey data in the presence of misclassification error is considered. Approaches which use external information on misclassification rates are reviewed, together with alternative models for measurement error. We define categorical instrumental variables and propose methods for the identification and estimation of models including such variables by viewing the model as a restricted latent class model. The numerical properties of the implied instrumental variable estimators of flow rates are studied using data from the Panel Study of Income Dynamics.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013106Description:
The standard error estimation method used for sample data in the U.S. Decennial Census from 1970 through 1990 yielded irregular results. For example, the method gave different standard error estimates for the "yes" and "no" response for the same binomial variable, when both standard error estimates should have been the same. If most respondents answered a binomial variable one way and a few answered the other way, the standard error estimate was much higher for the response with the most respondents. In addition, when 100 percent of respondents answered a question the same way, the standard error of this estimate was not zero, but was still quite high. Reporting average design effects which were weighted by the number of respondents that reported particular characteristics magnified the problem. An alternative to the random groups standard error estimate used in the U.S. census is suggested here.
Release date: 1997-08-18 - Articles and reports: 12-001-X19970013108Description:
We show how the use of matrix calculus can simplify the derivation of the linearization of the regression coefficient estimator and the regression estimator.
Release date: 1997-08-18
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