The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Yves G Berger - One of the best experts on this subject based on the ideXlab platform.
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a simple Variance Estimator of change for rotating repeated surveys an application to the european union statistics on income and living conditions household surveys
Journal of The Royal Statistical Society Series A-statistics in Society, 2016Co-Authors: Yves G Berger, Rodolphe PriamAbstract:type="main" xml:id="rssa12116-abs-0001"> A common problem is to compare two cross-sectional estimates for the same study variable taken on two different waves or occasions, and to judge whether the change observed is statistically significant. This involves the estimation of the sampling Variance of the Estimator of change. The estimation of this Variance would be relatively straightforward if cross-sectional estimates were based on the same sample. Unfortunately, samples are not completely overlapping, because of rotations used in repeated surveys. We propose a simple approach based on a multivariate (general) linear regression model. The Variance Estimator proposed is not a model-based Estimator. We show that the Estimator proposed is design consistent when the sampling fractions are negligible. It can accommodate stratified and two-stage sampling designs. The main advantage of the approach proposed is its simplicity and flexibility. It can be applied to a wide class of sampling designs and can be implemented with standard statistical regression techniques. Because of its flexibility, the approach proposed is well suited for the estimation of Variance for the European Union Statistics on Income and Living Conditions surveys. It allows us to use a common approach for Variance estimation for the different types of design. The approach proposed is a useful tool, because it involves only modelling skills and requires limited knowledge of survey sampling theory.
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a simple Variance Estimator of change for rotating repeated surveys an application to the european union statistics on income and living conditions household surveys
Journal of the Royal Statistical Society. Series A (General), 2015Co-Authors: Yves G Berger, Rodolphe PriamAbstract:A common problem is to compare two cross-sectional estimates for the same study variable taken on two different waves or occasions, and to judge whether the change observed is statistically significant. This involves the estimation of the sampling Variance of the Estimator of change. The estimation of this Variance would be relatively straightforward if cross-sectional estimates were based on the same sample. Unfortunately, samples are not completely overlapping, because of rotations used in repeated surveys. We propose a simple approach based on a multivariate (general) linear regression model. The Variance Estimator proposed is not a model-based Estimator. We show that the Estimator proposed is design consistent when the sampling fractions are negligible. It can accommodate stratified and two-stage sampling designs. The main advantage of the approach proposed is its simplicity and flexibility. It can be applied to a wide class of sampling designs and can be implemented with standard statistical regression techniques. Because of its flexibility, the approach proposed is well suited for the estimation of Variance for the European Union Statistics on Income and Living Conditions surveys. It allows us to use a common approach for Variance estimation for the different types of design. The approach proposed is a useful tool, because it involves only modelling skills and requires limited knowledge of survey sampling theory.
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a new replicate Variance Estimator for unequal probability sampling without replacement
Canadian Journal of Statistics-revue Canadienne De Statistique, 2013Co-Authors: Emilio L Escobar, Yves G BergerAbstract:We propose a new replicate Variance Estimator suitable for differentiable functions of estimated totals. The proposed Variance Estimator is defined for any unequal-probability without-replacement sampling design, it naturally includes finite population corrections and it allows two-stage sampling. We show its design-consistency and its close relationship with linearization Variance Estimators. When estimating a total, the proposed Estimator reduces to the Horvitz-Thompson Variance Estimator. Monte-Carlo simulations suggest that the proposed Variance Estimator is more stable than its replicate competitors. Resume Nous proposons un nouvel estimateur de Variance re-echantillonnes adapte a des fonctions derivables de totaux estimes. L'estimateur de Variance propose est defini pour tous les plans d’echantillonnage a probabilites inegales sans remise. Il comprend naturellement les corrections de population finie et il peut s'appliquer a l’echantillonnage a deux degres. Nous montrons sa convergence asymptotique sous le plan d’echantillonnage et sa relation avec les estimateurs linearises de Variance. Lors de l'estimation d'un total, l'estimateur propose se reduit a l'estimateur de Variance de Horvitz-Thompson. Des simulations suggerent que l'estimateur de Variance proposee est plus stable que ses concurrents.
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a jackknife Variance Estimator for self weighted two stage samples
2013Co-Authors: Emilio L Escobar, Yves G BergerAbstract:Self-weighted two-stage sampling designs are popular in practice as they simplify field-work. It is common in practice to compute Variance estimates only from the first sampling stage, neglecting the second stage. This omission may induce a bias in Variance estimation; especially in situations where there is low variabil- ity between clusters or when sampling fractions are non-negligible. We propose a design-consistent jackknife Variance Estimator that takes account of all stages via deletion of clusters and observations within clusters. The proposed jackknife can be used for a wide class of point Estimators. It does not need joint-inclusion prob- abilities and naturally includes finite population corrections. A simulation study shows that the proposed Estimator can be more accurate than standard jackknifes (Rao, Wu, and Yue (1992)) for self-weighted two-stage sampling designs.
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a jackknife Variance Estimator for unistage stratified samples with unequal probabilities
Biometrika, 2007Co-Authors: Yves G BergerAbstract:Existing jackknife Variance Estimators used with sample surveys can seriously overestimate the true Variance under unistage stratified sampling without replacement with unequal probabilities. A novel jackknife Variance Estimator is proposed which is as numerically simple as existing jackknife Variance Estimators. Under certain regularity conditions, the proposed Variance Estimator is consistent under stratified sampling without replacement with unequal probabilities. The high entropy regularity condition necessary for consistency is shown to hold for the Rao--Sampford design. An empirical study of three unequal probability sampling designs supports our findings. Copyright 2007, Oxford University Press.
Jinyong Hahn - One of the best experts on this subject based on the ideXlab platform.
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a practical asymptotic Variance Estimator for two step semiparametric Estimators
The Review of Economics and Statistics, 2012Co-Authors: Daniel A Ackerberg, Xiaohong Chen, Jinyong HahnAbstract:Abstract The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric Variances using standard formulas derived in the well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations as if it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
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a practical asymptotic Variance Estimator for two step semiparametric Estimators
The Review of Economics and Statistics, 2011Co-Authors: Daniel A Ackerberg, Xiaohong Chen, Jinyong HahnAbstract:The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric Variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations "as if"it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
Peter C Austin - One of the best experts on this subject based on the ideXlab platform.
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Variance estimation when using inverse probability of treatment weighting iptw with survival analysis
Statistics in Medicine, 2016Co-Authors: Peter C AustinAbstract:Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of Variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of Variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three Variance estimation methods: (i) a naive model-based Variance Estimator; (ii) a robust sandwich-type Variance Estimator; and (iii) a bootstrap Variance Estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap Estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other Estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Chris J Skinner - One of the best experts on this subject based on the ideXlab platform.
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a jackknife Variance Estimator for unequal probability sampling
Journal of The Royal Statistical Society Series B-statistical Methodology, 2005Co-Authors: Yves G Berger, Chris J SkinnerAbstract:The jackknife method is often used for Variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife Variance Estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this Estimator for a broad class of point Estimators. A Monte Carlo study shows how the proposed Estimator may improve on existing Estimators.
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a jackknife Variance Estimator for unequal probability sampling
Journal of The Royal Statistical Society Series B-statistical Methodology, 2005Co-Authors: Yves G Berger, Chris J SkinnerAbstract:The jackknife method is often used for Variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife Variance Estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this Estimator for a broad class of point Estimators. A Monte Carlo study shows how the proposed Estimator may improve on existing Estimators. Copyright 2005 Royal Statistical Society.
Daniel A Ackerberg - One of the best experts on this subject based on the ideXlab platform.
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a practical asymptotic Variance Estimator for two step semiparametric Estimators
The Review of Economics and Statistics, 2012Co-Authors: Daniel A Ackerberg, Xiaohong Chen, Jinyong HahnAbstract:Abstract The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric Variances using standard formulas derived in the well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations as if it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
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a practical asymptotic Variance Estimator for two step semiparametric Estimators
The Review of Economics and Statistics, 2011Co-Authors: Daniel A Ackerberg, Xiaohong Chen, Jinyong HahnAbstract:The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric Variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations "as if"it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.
