Filter results by
Search HelpKeyword(s)
Subject
Author(s)
Results
All (6)
All (6) ((6 results))
- Articles and reports: 11-536-X200900110805Description:
The estimation of a finite population distribution function is considered in the presence of nonresponse. An imputation approach is discussed which may also be interpreted as a form of weighted estimation. It is assumed that there are complete measurements on at least one auxiliary variable which is strongly related to the variable of interest. The paper is motivated by an application to the estimation of the distribution of hourly pay using data from the Labour Force Survey in the United Kingdom. In this case the main auxiliary variable is a proxy measure of the variable of interest. Techniques discussed include predictive mean matching, nearest neighbour imputation, fractional imputation and propensity score matching. Some theoretical and numerical properties of alternative procedures will be discussed.
Release date: 2009-08-11 - Articles and reports: 11-522-X20020016730Description:
A wide class of models of interest in social and economic research can be represented by specifying a parametric structure for the covariances of observed variables. The availability of software, such as LISREL (Jöreskog and Sörbom 1988) and EQS (Bentler 1995), has enabled these models to be fitted to survey data in many applications. In this paper, we consider approaches to inference about such models using survey data derived by complex sampling schemes. We consider evidence of finite sample biases in parameter estimation and ways to reduce such biases (Altonji and Segal 1996) and associated issues of efficiency of estimation, standard error estimation and testing. We use longitudinal data from the British Household Panel Survey for illustration. As these data are subject to attrition, we also consider the issue of how to use nonresponse weights in the modelling.
Release date: 2004-09-13 - 3. Estimation of a measure of disclosure risk for survey microdata under unequal probability sampling ArchivedArticles and reports: 12-001-X20030026784Description:
Skinner and Elliot (2002) proposed a simple measure of disclosure risk for survey microdata and showed how to estimate this measure under sampling with equal probabilities. In this paper we show how their results on point estimation and variance estimation may be extended to handle unequal probability sampling. Our approach assumes a Poisson sampling design. Comments are made about the possible impact of departures from this assumption.
Release date: 2004-01-27 - 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-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 - 6. Multi-way stratification by linear programming ArchivedArticles and reports: 12-001-X199400114430Description:
Rao and Nigam (1990, 1992) showed how a class of controlled sampling designs can be implemented using linear programming. In this article their approach is applied to multi-way stratification. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples and by evaluating mean squared errors. The proposed approach is relatively simple to use and appears to have reasonable mean squared error properties. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. Variance estimation is also considered.
Release date: 1994-06-15
Stats in brief (0)
Stats in brief (0) (0 results)
No content available at this time.
Articles and reports (6)
Articles and reports (6) ((6 results))
- Articles and reports: 11-536-X200900110805Description:
The estimation of a finite population distribution function is considered in the presence of nonresponse. An imputation approach is discussed which may also be interpreted as a form of weighted estimation. It is assumed that there are complete measurements on at least one auxiliary variable which is strongly related to the variable of interest. The paper is motivated by an application to the estimation of the distribution of hourly pay using data from the Labour Force Survey in the United Kingdom. In this case the main auxiliary variable is a proxy measure of the variable of interest. Techniques discussed include predictive mean matching, nearest neighbour imputation, fractional imputation and propensity score matching. Some theoretical and numerical properties of alternative procedures will be discussed.
Release date: 2009-08-11 - Articles and reports: 11-522-X20020016730Description:
A wide class of models of interest in social and economic research can be represented by specifying a parametric structure for the covariances of observed variables. The availability of software, such as LISREL (Jöreskog and Sörbom 1988) and EQS (Bentler 1995), has enabled these models to be fitted to survey data in many applications. In this paper, we consider approaches to inference about such models using survey data derived by complex sampling schemes. We consider evidence of finite sample biases in parameter estimation and ways to reduce such biases (Altonji and Segal 1996) and associated issues of efficiency of estimation, standard error estimation and testing. We use longitudinal data from the British Household Panel Survey for illustration. As these data are subject to attrition, we also consider the issue of how to use nonresponse weights in the modelling.
Release date: 2004-09-13 - 3. Estimation of a measure of disclosure risk for survey microdata under unequal probability sampling ArchivedArticles and reports: 12-001-X20030026784Description:
Skinner and Elliot (2002) proposed a simple measure of disclosure risk for survey microdata and showed how to estimate this measure under sampling with equal probabilities. In this paper we show how their results on point estimation and variance estimation may be extended to handle unequal probability sampling. Our approach assumes a Poisson sampling design. Comments are made about the possible impact of departures from this assumption.
Release date: 2004-01-27 - 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-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 - 6. Multi-way stratification by linear programming ArchivedArticles and reports: 12-001-X199400114430Description:
Rao and Nigam (1990, 1992) showed how a class of controlled sampling designs can be implemented using linear programming. In this article their approach is applied to multi-way stratification. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples and by evaluating mean squared errors. The proposed approach is relatively simple to use and appears to have reasonable mean squared error properties. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. Variance estimation is also considered.
Release date: 1994-06-15
Journals and periodicals (0)
Journals and periodicals (0) (0 results)
No content available at this time.
- Date modified: