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All (13)

All (13) (0 to 10 of 13 results)

1. Jean-Claude Deville’s contributions to survey theory and official statistics
Articles and reports: 12-001-X202300200017
Description: Jean-Claude Deville, who passed away in October 2021, was one of the most influential researchers in the field of survey statistics over the past 40 years. This article traces some of...
Description: Jean-Claude Deville, who passed away in October 2021, was one of the most influential researchers in the field of survey statistics over the past 40 years. This article traces some of his contributions that have had a profound impact on both survey theory and practice. This article will cover the topics of balanced sampling using the cube method, calibration, the weight-sharing method, the development of variance expressions of complex estimators using influence function and quota sampling.
More
Release date: 2024-01-03
2. Non-response follow-up for business surveys
Articles and reports: 12-001-X202200100006
Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods...
Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

More
Release date: 2022-06-21
3. A note on multiply robust predictive mean matching imputation with complex survey data
Articles and reports: 12-001-X202100100009
Description:
Predictive mean matching is a commonly used imputation procedure for addressing the problem of item nonresponse in surveys. The customary approach relies upon the specification of a...
Description:
Predictive mean matching is a commonly used imputation procedure for addressing the problem of item nonresponse in surveys. The customary approach relies upon the specification of a single outcome regression model. In this note, we propose a novel predictive mean matching procedure that allows the user to specify multiple outcome regression models. The resulting estimator is multiply robust in the sense that it remains consistent if one of the specified outcome regression models is correctly specified. The results from a simulation study suggest that the proposed method performs well in terms of bias and efficiency.
More
Release date: 2021-06-24
4. A note on the concept of invariance in two-phase sampling designs Archived
Articles and reports: 12-001-X201600214662
Description:
Two-phase sampling designs are often used in surveys when the sampling frame contains little or no auxiliary information. In this note, we shed some light on the concept of invariance...
Description:
Two-phase sampling designs are often used in surveys when the sampling frame contains little or no auxiliary information. In this note, we shed some light on the concept of invariance, which is often mentioned in the context of two-phase sampling designs. We define two types of invariant two-phase designs: strongly invariant and weakly invariant two-phase designs. Some examples are given. Finally, we describe the implications of strong and weak invariance from an inference point of view.
More
Release date: 2016-12-20
5. A method of determining the winsorization threshold, with an application to domain estimation Archived
Articles and reports: 12-001-X201500114199
Description:
In business surveys, it is not unusual to collect economic variables for which the distribution is highly skewed. In this context, winsorization is often used to treat the problem of...
Description:
In business surveys, it is not unusual to collect economic variables for which the distribution is highly skewed. In this context, winsorization is often used to treat the problem of influential values. This technique requires the determination of a constant that corresponds to the threshold above which large values are reduced. In this paper, we consider a method of determining the constant which involves minimizing the largest estimated conditional bias in the sample. In the context of domain estimation, we also propose a method of ensuring consistency between the domain-level winsorized estimates and the population-level winsorized estimate. The results of two simulation studies suggest that the proposed methods lead to winsorized estimators that have good bias and relative efficiency properties.
More
Release date: 2015-06-29
6. An interesting property of the entropy of some sampling designs Archived
Articles and reports: 12-001-X201000211385
Description:
In this short note, we show that simple random sampling without replacement and Bernoulli sampling have approximately the same entropy when the population size is large. An empirical...
Description:
In this short note, we show that simple random sampling without replacement and Bernoulli sampling have approximately the same entropy when the population size is large. An empirical example is given as an illustration.
More
Release date: 2010-12-21
7. The effect of nonresponse adjustments on variance estimation Archived
Articles and reports: 12-001-X201000111246
Description:
Many surveys employ weight adjustment procedures to reduce nonresponse bias. These adjustments make use of available auxiliary data. This paper addresses the issue of jackknife...
Description:
Many surveys employ weight adjustment procedures to reduce nonresponse bias. These adjustments make use of available auxiliary data. This paper addresses the issue of jackknife variance estimation for estimators that have been adjusted for nonresponse. Using the reverse approach for variance estimation proposed by Fay (1991) and Shao and Steel (1999), we study the effect of not re-calculating the nonresponse weight adjustment within each jackknife replicate. We show that the resulting 'shortcut' jackknife variance estimator tends to overestimate the true variance of point estimators in the case of several weight adjustment procedures used in practice. These theoretical results are confirmed through a simulation study where we compare the shortcut jackknife variance estimator with the full jackknife variance estimator obtained by re-calculating the nonresponse weight adjustment within each jackknife replicate.
More
Release date: 2010-06-29
8. Jackknife variance estimation in the presence of imputed data Archived
Articles and reports: 11-536-X200900110812
Description:
Variance estimation in the presence of imputed data has been widely studied in the literature. It is well known that treating the imputed values as if they were observed could lead to...
Description:
Variance estimation in the presence of imputed data has been widely studied in the literature. It is well known that treating the imputed values as if they were observed could lead to serious underestimation of the variance of the imputed estimator. Several approaches/techniques have been developed in recent years. In particular, Rao and Shao (1992) have proposed an adjusted jackknife that works well when the sampling fraction is small. However, in many situations, this condition is not satisfied. As a result, the Rao-Shao adjusted jackknife may lead to invalid variance estimators. To overcome this problem, Lee, Rancourt and Särndal (1995) have proposed a simple correction to the Rao-Shao adjusted jackknife. In this presentation, we discuss the properties of the resulting variance estimator under stratified simple random sampling without replacement. Also, using the reverse approach developed by Shao and Steel (1999), we consider another variance estimator that works well when the sampling fractions are not negligible. The case of unequal probability sampling designs such as proportional-to-size-designs will be briefly discussed.
More
Release date: 2009-08-11
9. Variance estimation for a ratio in the presence of imputed data Archived
Articles and reports: 12-001-X200700210493
Description:
In this paper, we study the problem of variance estimation for a ratio of two totals when marginal random hot deck imputation has been used to fill in missing data. We consider two...
Description:
In this paper, we study the problem of variance estimation for a ratio of two totals when marginal random hot deck imputation has been used to fill in missing data. We consider two approaches to inference. In the first approach, the validity of an imputation model is required. In the second approach, the validity of an imputation model is not required but response probabilities need to be estimated, in which case the validity of a nonresponse model is required. We derive variance estimators under two distinct frameworks: the customary two-phase framework and the reverse framework.
More
Release date: 2008-01-03
10. A nonresponse model approach to inference under imputation for missing survey data Archived
Articles and reports: 12-001-X20060019257
Description:
In the presence of item nonreponse, two approaches have been traditionally used to make inference on parameters of interest. The first approach assumes uniform response within...
Description:
In the presence of item nonreponse, two approaches have been traditionally used to make inference on parameters of interest. The first approach assumes uniform response within imputation cells whereas the second approach assumes ignorable response but make use of a model on the variable of interest as the basis for inference. In this paper, we propose a third appoach that assumes a specified ignorable response mechanism without having to specify a model on the variable of interest. In this case, we show how to obtain imputed values which lead to estimators of a total that are approximately unbiased under the proposed approach as well as the second approach. Variance estimators of the imputed estimators that are approximately unbiased are also obtained using an approach of Fay (1991) in which the order of sampling and response is reversed. Finally, simulation studies are conducted to investigate the finite sample performance of the methods in terms of bias and mean square error.
More
Release date: 2006-07-20

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Articles and reports (13)

Articles and reports (13) (0 to 10 of 13 results)

1. Jean-Claude Deville’s contributions to survey theory and official statistics
Articles and reports: 12-001-X202300200017
Description: Jean-Claude Deville, who passed away in October 2021, was one of the most influential researchers in the field of survey statistics over the past 40 years. This article traces some of...
Description: Jean-Claude Deville, who passed away in October 2021, was one of the most influential researchers in the field of survey statistics over the past 40 years. This article traces some of his contributions that have had a profound impact on both survey theory and practice. This article will cover the topics of balanced sampling using the cube method, calibration, the weight-sharing method, the development of variance expressions of complex estimators using influence function and quota sampling.
More
Release date: 2024-01-03
2. Non-response follow-up for business surveys
Articles and reports: 12-001-X202200100006
Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods...
Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

More
Release date: 2022-06-21
3. A note on multiply robust predictive mean matching imputation with complex survey data
Articles and reports: 12-001-X202100100009
Description:
Predictive mean matching is a commonly used imputation procedure for addressing the problem of item nonresponse in surveys. The customary approach relies upon the specification of a...
Description:
Predictive mean matching is a commonly used imputation procedure for addressing the problem of item nonresponse in surveys. The customary approach relies upon the specification of a single outcome regression model. In this note, we propose a novel predictive mean matching procedure that allows the user to specify multiple outcome regression models. The resulting estimator is multiply robust in the sense that it remains consistent if one of the specified outcome regression models is correctly specified. The results from a simulation study suggest that the proposed method performs well in terms of bias and efficiency.
More
Release date: 2021-06-24
4. A note on the concept of invariance in two-phase sampling designs Archived
Articles and reports: 12-001-X201600214662
Description:
Two-phase sampling designs are often used in surveys when the sampling frame contains little or no auxiliary information. In this note, we shed some light on the concept of invariance...
Description:
Two-phase sampling designs are often used in surveys when the sampling frame contains little or no auxiliary information. In this note, we shed some light on the concept of invariance, which is often mentioned in the context of two-phase sampling designs. We define two types of invariant two-phase designs: strongly invariant and weakly invariant two-phase designs. Some examples are given. Finally, we describe the implications of strong and weak invariance from an inference point of view.
More
Release date: 2016-12-20
5. A method of determining the winsorization threshold, with an application to domain estimation Archived
Articles and reports: 12-001-X201500114199
Description:
In business surveys, it is not unusual to collect economic variables for which the distribution is highly skewed. In this context, winsorization is often used to treat the problem of...
Description:
In business surveys, it is not unusual to collect economic variables for which the distribution is highly skewed. In this context, winsorization is often used to treat the problem of influential values. This technique requires the determination of a constant that corresponds to the threshold above which large values are reduced. In this paper, we consider a method of determining the constant which involves minimizing the largest estimated conditional bias in the sample. In the context of domain estimation, we also propose a method of ensuring consistency between the domain-level winsorized estimates and the population-level winsorized estimate. The results of two simulation studies suggest that the proposed methods lead to winsorized estimators that have good bias and relative efficiency properties.
More
Release date: 2015-06-29
6. An interesting property of the entropy of some sampling designs Archived
Articles and reports: 12-001-X201000211385
Description:
In this short note, we show that simple random sampling without replacement and Bernoulli sampling have approximately the same entropy when the population size is large. An empirical...
Description:
In this short note, we show that simple random sampling without replacement and Bernoulli sampling have approximately the same entropy when the population size is large. An empirical example is given as an illustration.
More
Release date: 2010-12-21
7. The effect of nonresponse adjustments on variance estimation Archived
Articles and reports: 12-001-X201000111246
Description:
Many surveys employ weight adjustment procedures to reduce nonresponse bias. These adjustments make use of available auxiliary data. This paper addresses the issue of jackknife...
Description:
Many surveys employ weight adjustment procedures to reduce nonresponse bias. These adjustments make use of available auxiliary data. This paper addresses the issue of jackknife variance estimation for estimators that have been adjusted for nonresponse. Using the reverse approach for variance estimation proposed by Fay (1991) and Shao and Steel (1999), we study the effect of not re-calculating the nonresponse weight adjustment within each jackknife replicate. We show that the resulting 'shortcut' jackknife variance estimator tends to overestimate the true variance of point estimators in the case of several weight adjustment procedures used in practice. These theoretical results are confirmed through a simulation study where we compare the shortcut jackknife variance estimator with the full jackknife variance estimator obtained by re-calculating the nonresponse weight adjustment within each jackknife replicate.
More
Release date: 2010-06-29
8. Jackknife variance estimation in the presence of imputed data Archived
Articles and reports: 11-536-X200900110812
Description:
Variance estimation in the presence of imputed data has been widely studied in the literature. It is well known that treating the imputed values as if they were observed could lead to...
Description:
Variance estimation in the presence of imputed data has been widely studied in the literature. It is well known that treating the imputed values as if they were observed could lead to serious underestimation of the variance of the imputed estimator. Several approaches/techniques have been developed in recent years. In particular, Rao and Shao (1992) have proposed an adjusted jackknife that works well when the sampling fraction is small. However, in many situations, this condition is not satisfied. As a result, the Rao-Shao adjusted jackknife may lead to invalid variance estimators. To overcome this problem, Lee, Rancourt and Särndal (1995) have proposed a simple correction to the Rao-Shao adjusted jackknife. In this presentation, we discuss the properties of the resulting variance estimator under stratified simple random sampling without replacement. Also, using the reverse approach developed by Shao and Steel (1999), we consider another variance estimator that works well when the sampling fractions are not negligible. The case of unequal probability sampling designs such as proportional-to-size-designs will be briefly discussed.
More
Release date: 2009-08-11
9. Variance estimation for a ratio in the presence of imputed data Archived
Articles and reports: 12-001-X200700210493
Description:
In this paper, we study the problem of variance estimation for a ratio of two totals when marginal random hot deck imputation has been used to fill in missing data. We consider two...
Description:
In this paper, we study the problem of variance estimation for a ratio of two totals when marginal random hot deck imputation has been used to fill in missing data. We consider two approaches to inference. In the first approach, the validity of an imputation model is required. In the second approach, the validity of an imputation model is not required but response probabilities need to be estimated, in which case the validity of a nonresponse model is required. We derive variance estimators under two distinct frameworks: the customary two-phase framework and the reverse framework.
More
Release date: 2008-01-03
10. A nonresponse model approach to inference under imputation for missing survey data Archived
Articles and reports: 12-001-X20060019257
Description:
In the presence of item nonreponse, two approaches have been traditionally used to make inference on parameters of interest. The first approach assumes uniform response within...
Description:
In the presence of item nonreponse, two approaches have been traditionally used to make inference on parameters of interest. The first approach assumes uniform response within imputation cells whereas the second approach assumes ignorable response but make use of a model on the variable of interest as the basis for inference. In this paper, we propose a third appoach that assumes a specified ignorable response mechanism without having to specify a model on the variable of interest. In this case, we show how to obtain imputed values which lead to estimators of a total that are approximately unbiased under the proposed approach as well as the second approach. Variance estimators of the imputed estimators that are approximately unbiased are also obtained using an approach of Fay (1991) in which the order of sampling and response is reversed. Finally, simulation studies are conducted to investigate the finite sample performance of the methods in terms of bias and mean square error.
More
Release date: 2006-07-20

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Date modified:: 2025-05-25