3篇
您的检索式：作者名="Danny Pfeffermann"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Multivariate small area estimation under nonignorable nonresponse显示文摘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. | Danny Pfeffermann Michael Sverchkov | 2019 | Statistical Theory and Related Fields2019，3，2： | 0 |
| 2 | How I became a statistician — thank you speech at birthday dinner显示文摘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, and General Statistics: A Celebration of Professor Danny Pfeffermann’s75th Birthday. | Danny Pfeffermann | 2018 | Statistical Theory and Related Fields2018，2，2： | 0 |
| 3 | Model-based small area estimation with no samples within the areas,by benchmarking to marginal cross-sectional and time-series estimates显示文摘Official monthly U.S.labour 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 estimation.The administrative data,however,are poor in coverage and have conceptual deficiencies.Past 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 level.Benchmarking the sub-State HB estimates each month to the State model dependent estimates helps to correct for an overall bias,but not in individual areas.In this article we propose benchmarking additionally to the annual model-dependent area estimates.The 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 production.Variance estimates,which account for sampling errors and the errors of the model dependent estimators are developed.Data for sub-State areas in the State of Arizona are used for illustration.Although the methodology developed in this article stems from a particular(but very important)application,it is general and applicable to other similar problems. | Danny Pfeffermann Michael Sverchkov Richard Tiller Lizhi Liu | 2020 | Statistical Theory and Related Fields2020，4，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员