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- Articles and reports: 75F0002M2000006Description:
This paper discusses methods and tools considered and used to produce cross-sectional estimates based on the combination of two longitudinal panels for the Survey of Labour and Income Dynamics (SLID).
Release date: 2000-10-05 - Articles and reports: 75F0002M2000004Description:
This paper describes the methodology for the longitudinal and cross-sectional weights produced by the Survey of Labour and Income Dynamics (SLID). It also presents problems the survey has encountered and proposed solutions.
Release date: 2000-08-31 - 3. Estimation of census adjustment factors ArchivedArticles and reports: 12-001-X20000015176Description:
A components-of-variance approach and an estimated covariance error structure were used in constructing predictors of adjustment factors for the 1990 Decennial Census. The variability of the estimated covariance matrix is the suspected cause of certain anomalies that appeared in the regression estimation and in the estimated adjustment factors. We investigate alternative prediction methods and propose a procedure that is less influenced by variability in the estimated covariance matrix. The proposed methodology is applied to a data set composed of 336 adjustment factors from the 1990 Post Enumeration Survey.
Release date: 2000-08-30 - 4. Census coverage error: A demographic evaluation ArchivedArticles and reports: 12-001-X20000015177Description:
The 1996 Canadian Census is adjusted for coverage error as estimated primarily through the Reverse Record Check (RRC). In this paper, we will show how there is a wealth of additional information from the 1996 Reverse Record Check of direct value to population estimation. Beyond its ability to estimate coverage error, it is possible to extend the Reverse Record Check classification results to obtain an alternative estimate of demographic growth - potentially decomposed by component. This added feature of the Reverse Record Check provides promise in the evaluation of estimated census coverage error as well as insight as to possible problems in the estimation of selected components in the population estimates program.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015179Description:
This paper suggests estimating the conditional mean squared error of small area estimators to evaluate their accuracy. This mean squared error is conditional in the sense that it measures the variability with respect to the sampling design for a particular realization of the smoothing model underlying the small area estimators. An unbiased estimators for the conditional mean squared error is easily constructed using Stein's Lemma for the expectation of normal random variables.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015181Description:
Samples from hidden and hard-to-access human populations are often obtained by procedures in which social links are followed from one respondent to another. Inference from the sample to the larger population of interest can be affected by the link-tracing design and the type of data it produces. The population with its social network structure can be modeled as a stochastic graph with a joint distribution of node values representing characteristics of individuals and arc indicators representing social relationships between individuals.
Release date: 2000-08-30 - 7. Calibration and restricted weights ArchivedArticles and reports: 12-001-X20000015182Description:
To better understand the impact of imposing a restricted region on calibration weights, the author reviews the latter's aymptotic behaviour. Necessary and sufficient conditions are provided for the existence of a solution to the calibration equation with weights within given intervals.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015183Description:
For surveys which involve more than one stage of data collection, one method recommended for adjusting weights for nonresponse (after the first stage of data collection) entails utilizing auxiliary variables (from previous stages of data collection) which are identified as predictors of nonresponse.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015184Description:
Survey statisticians frequently use superpopulation linear regression models. The Gauss-Markov theorem, assuming fixed regressors or conditioning on observed values of regressors, asserts that the standard estimators of regression coefficients are best linear unbiased.
Release date: 2000-08-30 - 10. A donor imputation system to create a census database fully adjusted for underenumeration ArchivedSurveys and statistical programs – Documentation: 11-522-X19990015668Description:
Following the problems with estimating underenumeration in the 1991 Census of England and Wales the aim for the 2001 Census is to create a database that is fully adjusted to net underenumeration. To achieve this, the paper investigates weighted donor imputation methodology that utilises information from both the census and census coverage survey (CCS). The US Census Bureau has considered a similar approach for their 2000 Census (see Isaki et al 1998). The proposed procedure distinguishes between individuals who are not counted by the census because their household is missed and those who are missed in counted households. Census data is linked to data from the CCS. Multinomial logistic regression is used to estimate the probabilities that households are missed by the census and the probabilities that individuals are missed in counted households. Household and individual coverage weights are constructed from the estimated probabilities and these feed into the donor imputation procedure.
Release date: 2000-03-02
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- Articles and reports: 75F0002M2000006Description:
This paper discusses methods and tools considered and used to produce cross-sectional estimates based on the combination of two longitudinal panels for the Survey of Labour and Income Dynamics (SLID).
Release date: 2000-10-05 - Articles and reports: 75F0002M2000004Description:
This paper describes the methodology for the longitudinal and cross-sectional weights produced by the Survey of Labour and Income Dynamics (SLID). It also presents problems the survey has encountered and proposed solutions.
Release date: 2000-08-31 - 3. Estimation of census adjustment factors ArchivedArticles and reports: 12-001-X20000015176Description:
A components-of-variance approach and an estimated covariance error structure were used in constructing predictors of adjustment factors for the 1990 Decennial Census. The variability of the estimated covariance matrix is the suspected cause of certain anomalies that appeared in the regression estimation and in the estimated adjustment factors. We investigate alternative prediction methods and propose a procedure that is less influenced by variability in the estimated covariance matrix. The proposed methodology is applied to a data set composed of 336 adjustment factors from the 1990 Post Enumeration Survey.
Release date: 2000-08-30 - 4. Census coverage error: A demographic evaluation ArchivedArticles and reports: 12-001-X20000015177Description:
The 1996 Canadian Census is adjusted for coverage error as estimated primarily through the Reverse Record Check (RRC). In this paper, we will show how there is a wealth of additional information from the 1996 Reverse Record Check of direct value to population estimation. Beyond its ability to estimate coverage error, it is possible to extend the Reverse Record Check classification results to obtain an alternative estimate of demographic growth - potentially decomposed by component. This added feature of the Reverse Record Check provides promise in the evaluation of estimated census coverage error as well as insight as to possible problems in the estimation of selected components in the population estimates program.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015179Description:
This paper suggests estimating the conditional mean squared error of small area estimators to evaluate their accuracy. This mean squared error is conditional in the sense that it measures the variability with respect to the sampling design for a particular realization of the smoothing model underlying the small area estimators. An unbiased estimators for the conditional mean squared error is easily constructed using Stein's Lemma for the expectation of normal random variables.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015181Description:
Samples from hidden and hard-to-access human populations are often obtained by procedures in which social links are followed from one respondent to another. Inference from the sample to the larger population of interest can be affected by the link-tracing design and the type of data it produces. The population with its social network structure can be modeled as a stochastic graph with a joint distribution of node values representing characteristics of individuals and arc indicators representing social relationships between individuals.
Release date: 2000-08-30 - 7. Calibration and restricted weights ArchivedArticles and reports: 12-001-X20000015182Description:
To better understand the impact of imposing a restricted region on calibration weights, the author reviews the latter's aymptotic behaviour. Necessary and sufficient conditions are provided for the existence of a solution to the calibration equation with weights within given intervals.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015183Description:
For surveys which involve more than one stage of data collection, one method recommended for adjusting weights for nonresponse (after the first stage of data collection) entails utilizing auxiliary variables (from previous stages of data collection) which are identified as predictors of nonresponse.
Release date: 2000-08-30 - Articles and reports: 12-001-X20000015184Description:
Survey statisticians frequently use superpopulation linear regression models. The Gauss-Markov theorem, assuming fixed regressors or conditioning on observed values of regressors, asserts that the standard estimators of regression coefficients are best linear unbiased.
Release date: 2000-08-30 - Articles and reports: 12-001-X19990024879Description:
Godambe and Thompson consider the problem of confidence intervals in survey sampling. They first review the use of estimating functions to obtain model robust pivotal quantities and associated confidence intervals, and then discuss the adaptation of this approach to the survey sampling context. Details are worked out for some more specific types of models, and an empirical comparison of this approach with more conventional methods is presented.
Release date: 2000-03-01
Reference (6)
Reference (6) ((6 results))
- 1. A donor imputation system to create a census database fully adjusted for underenumeration ArchivedSurveys and statistical programs – Documentation: 11-522-X19990015668Description:
Following the problems with estimating underenumeration in the 1991 Census of England and Wales the aim for the 2001 Census is to create a database that is fully adjusted to net underenumeration. To achieve this, the paper investigates weighted donor imputation methodology that utilises information from both the census and census coverage survey (CCS). The US Census Bureau has considered a similar approach for their 2000 Census (see Isaki et al 1998). The proposed procedure distinguishes between individuals who are not counted by the census because their household is missed and those who are missed in counted households. Census data is linked to data from the CCS. Multinomial logistic regression is used to estimate the probabilities that households are missed by the census and the probabilities that individuals are missed in counted households. Household and individual coverage weights are constructed from the estimated probabilities and these feed into the donor imputation procedure.
Release date: 2000-03-02 - Surveys and statistical programs – Documentation: 11-522-X19990015672Description:
Data fusion as discussed here means to create a set of data on not jointly observed variables from two different sources. Suppose for instance that observations are available for (X,Z) on a set of individuals and for (Y,Z) on a different set of individuals. Each of X, Y and Z may be a vector variable. The main purpose is to gain insight into the joint distribution of (X,Y) using Z as a so-called matching variable. At first however, it is attempted to recover as much information as possible on the joint distribution of (X,Y,Z) from the distinct sets of data. Such fusions can only be done at the cost of implementing some distributional properties for the fused data. These are conditional independencies given the matching variables. Fused data are typically discussed from the point of view of how appropriate this underlying assumption is. Here we give a different perspective. We formulate the problem as follows: how can distributions be estimated in situations when only observations from certain marginal distributions are available. It can be solved by applying the maximum entropy criterium. We show in particular that data created by fusing different sources can be interpreted as a special case of this situation. Thus, we derive the needed assumption of conditional independence as a consequence of the type of data available.
Release date: 2000-03-02 - Surveys and statistical programs – Documentation: 11-522-X19990015674Description:
The effect of the environment on health is of increasing concern, in particular the effects of the release of industrial pollutants into the air, the ground and into water. An assessment of the risks to public health of any particular pollution source is often made using the routine health, demographic and environmental data collected by government agencies. These datasets have important differences in sampling geography and in sampling epochs which affect the epidemiological analyses which draw them together. In the UK, health events are recorded for individuals, giving cause codes, a data of diagnosis or death, and using the unit postcode as a geographical reference. In contrast, small area demographic data are recorded only at the decennial census, and released as area level data in areas distinct from postcode geography. Environmental exposure data may be available at yet another resolution, depending on the type of exposure and the source of the measurements.
Release date: 2000-03-02 - Surveys and statistical programs – Documentation: 11-522-X19990015680Description:
To augment the amount of available information, data from different sources are increasingly being combined. These databases are often combined using record linkage methods. When there is no unique identifier, a probabilistic linkage is used. In that case, a record on a first file is associated with a probability that is linked to a record on a second file, and then a decision is taken on whether a possible link is a true link or not. This usually requires a non-negligible amount of manual resolution. It might then be legitimate to evaluate if manual resolution can be reduced or even eliminated. This issue is addressed in this paper where one tries to produce an estimate of a total (or a mean) of one population, when using a sample selected from another population linked somehow to the first population. In other words, having two populations linked through probabilistic record linkage, we try to avoid any decision concerning the validity of links and still be able to produce an unbiased estimate for a total of the one of two populations. To achieve this goal, we suggest the use of the Generalised Weight Share Method (GWSM) described by Lavallée (1995).
Release date: 2000-03-02 - 5. Simultaneous calibration of several surveys ArchivedSurveys and statistical programs – Documentation: 11-522-X19990015684Description:
Often, the same information is gathered almost simultaneously for several different surveys. In France, this practice is institutionalized for household surveys that have a common set of demographic variables, i.e., employment, residence and income. These variables are important co-factors for the variables of interest in each survey, and if used carefully, can reinforce the estimates derived from each survey. Techniques for calibrating uncertain data can apply naturally in this context. This involves finding the best unbiased estimator in common variables and calibrating each survey based on that estimator. The estimator thus obtained in each survey is always a linear estimator, the weightings of which can be easily explained and the variance can be obtained with no new problems, as can the variance estimate. To supplement the list of regression estimators, this technique can also be seen as a ridge-regression estimator, or as a Bayesian-regression estimator.
Release date: 2000-03-02 - Surveys and statistical programs – Documentation: 11-522-X19990015690Description:
The artificial sample was generated in two steps. The first step, based on a master panel, was a Multiple Correspondence Analysis (MCA) carried out on basic variables. Then, "dummy" individuals were generated randomly using the distribution of each "significant" factor in the analysis. Finally, for each individual, a value was generated for each basic variable most closely linked to one of the previous factors. This method ensured that sets of variables were drawn independently. The second step consisted in grafting some other data bases, based on certain property requirements. A variable was generated to be added on the basis of its estimated distribution, using a generalized linear model for common variables and those already added. The same procedure was then used to graft the other samples. This method was applied to the generation of an artificial sample taken from two surveys. The artificial sample that was generated was validated using sample comparison testing. The results were positive, demonstrating the feasibility of this method.
Release date: 2000-03-02
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