Results
All (18)
All (18) (0 to 10 of 18 results)
- 1. Panel surveys: Adding the fourth dimension ArchivedArticles and reports: 12-001-X199300214452Description:
Surveys across time can serve many objectives. The first half of the paper reviews the abilities of alternative survey designs across time - repeated surveys, panel surveys, rotating panel surveys and split panel surveys - to meet these objectives. The second half concentrates on panel surveys. It discusses the decisions that need to be made in designing a panel survey, the problems of wave nonresponse, time-in-sample bias and the seam effect, and some methods for the longitudinal analysis of panel survey data.
Release date: 1993-12-15 - 2. An alternative view of forest sampling ArchivedArticles and reports: 12-001-X199300214453Description:
A generalized concept is presented for all of the commonly used methods of forest sampling. The concept views the forest as a two-dimensional picture which is cut up into pieces like a jigsaw puzzle, with the pieces defined by the individual selection probabilities of the trees in the forest. This concept results in a finite number of independently selected sample units, in contrast to every other generalized conceptualization of forest sampling presented to date.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214454Description:
This study covers such imperfect frames in which no population unit has been excluded from the frame but an unspecified number of population units may have been included in the list an unspecified number of times each with a separate identification. When the availability of auxiliary information on any unit in the imperfect frame is not assumed, it is established that for estimation of a population ratio or a mean, the mean square errors of estimators based on the imperfect frame are less than those based on the perfect frame for simple random sampling when the sampling fractions of perfect and imperfect frames are the same. For estimation of a population total, however, this is not always true. Also, there are situations in which estimators of a ratio, a mean or a total based on smaller sampling fraction from imperfect frame can have smaller mean square error than those based on a larger sampling fraction from the perfect frame.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214455Description:
Post-stratification is a common technique for improving precision of estimators by using data items not available at the design stage of a survey. In large, complex samples, the vector of Horvitz-Thompson estimators of survey target variables and of post-stratum population sizes will, under appropriate conditions, be approximately multivariate normal. This large sample normality leads to a new post-stratified regression estimator, which is analogous to the linear regression estimator in simple random sampling. We derive the large sample design bias and mean squared errors of this new estimator, the standard post-stratified estimator, the Horvitz-Thompson estimator, and a ratio estimator. We use both real and artificial populations to study empirically the conditional and unconditional properties of the estimators in multistage sampling.
Release date: 1993-12-15 - 5. Optimum two-stage sample design for ratio estimators: Application to quality control - 1990 French census ArchivedArticles and reports: 12-001-X199300214456Description:
This study is based on the use of superpopulation models to anticipate, before data collection, the variance of a measure by ratio sampling. The method, based on models that are both simple and fairly realistic, produces expressions of varying complexity and then optimizes them, in some cases rigorously, in others approximately. The solution to the final problem discussed points up a rarely considered factor in sample design optimization: the cost related to collecting individual information.
Release date: 1993-12-15 - 6. Maximum likelihood estimation of constant multiplicative bias benchmarking model with application ArchivedArticles and reports: 12-001-X199300214457Description:
The maximum likelihood estimation of a non-linear benchmarking model, proposed by Laniel and Fyfe (1989; 1990), is considered. This model takes into account the biases and sampling errors associated with the original series. Since the maximum likelihood estimators of the model parameters are not obtainable in closed forms, two iterative procedures to find the maximum likelihood estimates are discussed. The closed form expressions for the asymptotic variances and covariances of the benchmarked series, and of the fitted values are also provided. The methodology is illustrated using published Canadian retail trade data.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214458Description:
In this article we report the results of fitting a state-space model to Canadian unemployment rates. The model assumes an additive decomposition of the population values into a trend, seasonal and irregular component and separate autoregressive relationships for the six survey error series corresponding to the six monthly panel estimators. The model includes rotation group effects and permits the design variances of the survey errors to change over time. The model is fitted at the small area level but it accounts for correlations between the component series of different areas. The robustness of estimators obtained under the model is achieved by imposing the constraint that the monthly aggregate model based estimators in a group of small areas for which the total sample size is sufficiently large coincide with the corresponding direct survey estimators. The performance of the model when fitted to the Atlantic provinces is assessed by a variety of diagnostic statistics and residual plots and by comparisons with estimators in current use.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214459Description:
Record linkage is the matching of records containing data on individuals, businesses or dwellings when a unique identifier is not available. Methods used in practice involve classification of record pairs as links and non-links using an automated procedure based on the theoretical framework introduced by Fellegi and Sunter (1969). The estimation of classification error rates is an important issue. Fellegi and Sunter provide a method for calculation of classification error rate estimates as a direct by-product of linkage. These model-based estimates are easier to produce than the estimates based on manual matching of samples that are typically used in practice. Properties of model-based classification error rate estimates obtained using three estimators of model parameters are compared.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214460Description:
Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300114471Description:
Binomial-Poisson and Poisson-Poisson sampling are introduced for use in forest sampling. Several estimators of the population total are discussed for these designs. Simulation comparisons of the properties of the estimators were made for three small forestry populations. A modification of the standard estimator used for Poisson sampling and a new estimator, called a modified Srivastava estimator, appear to be most efficient. The latter is unfortunately badly biased for all 3 populations.
Release date: 1993-06-15
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Articles and reports (18)
Articles and reports (18) (0 to 10 of 18 results)
- 1. Panel surveys: Adding the fourth dimension ArchivedArticles and reports: 12-001-X199300214452Description:
Surveys across time can serve many objectives. The first half of the paper reviews the abilities of alternative survey designs across time - repeated surveys, panel surveys, rotating panel surveys and split panel surveys - to meet these objectives. The second half concentrates on panel surveys. It discusses the decisions that need to be made in designing a panel survey, the problems of wave nonresponse, time-in-sample bias and the seam effect, and some methods for the longitudinal analysis of panel survey data.
Release date: 1993-12-15 - 2. An alternative view of forest sampling ArchivedArticles and reports: 12-001-X199300214453Description:
A generalized concept is presented for all of the commonly used methods of forest sampling. The concept views the forest as a two-dimensional picture which is cut up into pieces like a jigsaw puzzle, with the pieces defined by the individual selection probabilities of the trees in the forest. This concept results in a finite number of independently selected sample units, in contrast to every other generalized conceptualization of forest sampling presented to date.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214454Description:
This study covers such imperfect frames in which no population unit has been excluded from the frame but an unspecified number of population units may have been included in the list an unspecified number of times each with a separate identification. When the availability of auxiliary information on any unit in the imperfect frame is not assumed, it is established that for estimation of a population ratio or a mean, the mean square errors of estimators based on the imperfect frame are less than those based on the perfect frame for simple random sampling when the sampling fractions of perfect and imperfect frames are the same. For estimation of a population total, however, this is not always true. Also, there are situations in which estimators of a ratio, a mean or a total based on smaller sampling fraction from imperfect frame can have smaller mean square error than those based on a larger sampling fraction from the perfect frame.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214455Description:
Post-stratification is a common technique for improving precision of estimators by using data items not available at the design stage of a survey. In large, complex samples, the vector of Horvitz-Thompson estimators of survey target variables and of post-stratum population sizes will, under appropriate conditions, be approximately multivariate normal. This large sample normality leads to a new post-stratified regression estimator, which is analogous to the linear regression estimator in simple random sampling. We derive the large sample design bias and mean squared errors of this new estimator, the standard post-stratified estimator, the Horvitz-Thompson estimator, and a ratio estimator. We use both real and artificial populations to study empirically the conditional and unconditional properties of the estimators in multistage sampling.
Release date: 1993-12-15 - 5. Optimum two-stage sample design for ratio estimators: Application to quality control - 1990 French census ArchivedArticles and reports: 12-001-X199300214456Description:
This study is based on the use of superpopulation models to anticipate, before data collection, the variance of a measure by ratio sampling. The method, based on models that are both simple and fairly realistic, produces expressions of varying complexity and then optimizes them, in some cases rigorously, in others approximately. The solution to the final problem discussed points up a rarely considered factor in sample design optimization: the cost related to collecting individual information.
Release date: 1993-12-15 - 6. Maximum likelihood estimation of constant multiplicative bias benchmarking model with application ArchivedArticles and reports: 12-001-X199300214457Description:
The maximum likelihood estimation of a non-linear benchmarking model, proposed by Laniel and Fyfe (1989; 1990), is considered. This model takes into account the biases and sampling errors associated with the original series. Since the maximum likelihood estimators of the model parameters are not obtainable in closed forms, two iterative procedures to find the maximum likelihood estimates are discussed. The closed form expressions for the asymptotic variances and covariances of the benchmarked series, and of the fitted values are also provided. The methodology is illustrated using published Canadian retail trade data.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214458Description:
In this article we report the results of fitting a state-space model to Canadian unemployment rates. The model assumes an additive decomposition of the population values into a trend, seasonal and irregular component and separate autoregressive relationships for the six survey error series corresponding to the six monthly panel estimators. The model includes rotation group effects and permits the design variances of the survey errors to change over time. The model is fitted at the small area level but it accounts for correlations between the component series of different areas. The robustness of estimators obtained under the model is achieved by imposing the constraint that the monthly aggregate model based estimators in a group of small areas for which the total sample size is sufficiently large coincide with the corresponding direct survey estimators. The performance of the model when fitted to the Atlantic provinces is assessed by a variety of diagnostic statistics and residual plots and by comparisons with estimators in current use.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214459Description:
Record linkage is the matching of records containing data on individuals, businesses or dwellings when a unique identifier is not available. Methods used in practice involve classification of record pairs as links and non-links using an automated procedure based on the theoretical framework introduced by Fellegi and Sunter (1969). The estimation of classification error rates is an important issue. Fellegi and Sunter provide a method for calculation of classification error rate estimates as a direct by-product of linkage. These model-based estimates are easier to produce than the estimates based on manual matching of samples that are typically used in practice. Properties of model-based classification error rate estimates obtained using three estimators of model parameters are compared.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300214460Description:
Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.
Release date: 1993-12-15 - Articles and reports: 12-001-X199300114471Description:
Binomial-Poisson and Poisson-Poisson sampling are introduced for use in forest sampling. Several estimators of the population total are discussed for these designs. Simulation comparisons of the properties of the estimators were made for three small forestry populations. A modification of the standard estimator used for Poisson sampling and a new estimator, called a modified Srivastava estimator, appear to be most efficient. The latter is unfortunately badly biased for all 3 populations.
Release date: 1993-06-15
Journals and periodicals (0)
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