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How I became a statistician — thank you speech at birthday dinner

Statistical Theory and Related Fields 2018年第2期2卷 222-224页

作者： danny pfeffermann National Statistician and Director Central Bureau of StatisticsIsraelHebrew University of JerusalemIsraelUniversity of SouthamptonUK

This speech was delivered at the Banquet and Award Ceremony June 17, 2018, during SAE 2018– an international conference on ‘Small Area Estimation and Other Topics of Current Interest inSurveys, Official Statistics, ... 详细信息

关键词： dinner thank speech

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Multivariate small area estimation under nonignorable nonresponse

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Statistical Theory and Related Fields 2019年第2期3卷 213-223页

作者： danny pfeffermann Michael Sverchkov National Statistician and CBS Director JerusalemIsrael Department of Statistics Hebrew UniversityJerusalemIsrael Southampton Statistical Sciences Research Institute(S3RI) University of SouthamptonSouthamptonUK Bureau of Labor Statistics WashingtonDCUSA

We consider multivariate small area estimation under nonignorable, not missing at random(NMAR) nonresponse. We assume a response model that accounts for the different patterns ofthe observed outcomes, (which values are observed and which ones are missing), and estimatethe response probabilities by application of the Missing Information Principle (MIP). By this principle, we first derive the likelihood score equations for the case where the missing outcomes areactually observed, and then integrate out the unobserved outcomes from the score equationswith respect to the distribution holding for the missing data. The latter distribution is definedby the distribution fitted to the observed data for the respondents and the response model. Theintegrated score equations are then solved with respect to the unknown parameters indexingthe response model. Once the response probabilities have been estimated, we impute the missing outcomes from their appropriate distribution, yielding a complete data set with no missingvalues, which is used for predicting the target area means. A parametric bootstrap procedure isdeveloped for assessing the mean squared errors (MSE) of the resulting predictors. We illustratethe approach by a small simulation study.

关键词： Distribution of missing data imputation under nonignorable nonresponse missing information principle MSE estimation NMAR nonresponse

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Model-based small area estimation with no samples within the areas,by benchmarking to marginal cross-sectional and time-series estimates

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Statistical Theory and Related Fields 2020年第1期4卷 28-42页

作者： danny pfeffermann Michael Sverchkov Richard Tiller Lizhi Liu National Statistician and CBS Director JerusalemIsrael Department of Statistics Hebrew University of JerusalemJerusalemIsrael Southampton Statistical Sciences Research Institute University of SouthamptonSouthamptonUK Bureau of Labor Statistics WashingtonDCUSA

Official monthly *** force estimation at the sub-State level(mostly counties)is based on what is known as the‘Handbook’(HB)method,one of the earliest uses of administrative data for small area *** administrative data,however,are poor in coverage and have conceptual *** attempts to correct for the resulting bias of the HB estimates by informal(implicit)modelling have not been successful,due to the absence of regular direct monthly survey estimates at the sub-State *** the sub-State HB estimates each month to the State model dependent estimates helps to correct for an overall bias,but not in individual *** this article we propose benchmarking additionally to the annual model-dependent area *** annual models include known administrative data as covariates,and are used to define corresponding monthly sub-State models,which in turn enable producing monthly synthetic estimates as possible substitutes for the HB estimates in real time *** estimates,which account for sampling errors and the errors of the model dependent estimators are *** for sub-State areas in the State of Arizona are used for *** the methodology developed in this article stems from a particular(but very important)application,it is general and applicable to other similar problems.

关键词： Benchmarking Denton method mixed models variance estimation

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