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区域及区域内信息性概率抽样下的小区域估计AI 解读

In this article we show how to predict small-area means and obtain valid mean squared error estimators and confidence intervals when the areas represented in the sample are sampled with unequal probabilities possibly related to the true (unknown) area means and the sampling of units within the selected areas is with probabilities possibly related to the outcome values. Ignoring the effects of the sampling process on the distribution of the observed outcomes in such cases may…

满足基准约束的状态空间小区域估计AI 解读

This article shows how to benchmark small-area estimators, produced by fitting separate state–space models within the areas, to aggregates of the survey direct estimators within a group of areas. State–space models are used by the U.S. Bureau of Labor Statistics (BLS) for the production of all of the monthly employment and unemployment estimates in census divisions and the states. Computation of the benchmarked estimators and their variances is accomplished by incorporating…

信息性抽样下的多层模型AI 解读

We consider a model-dependent approach for multi-level modelling that accounts for informative probability sampling of first- and lower-level population units. The proposed approach consists of first extracting the hierarchical model holding for the sample data given the selected sample, as a function of the corresponding population model and the first- and lower-level sample selection probabilities, and then fitting the resulting sample model using Bayesian methods. An…

调查误差自相关的估计及其在小区域趋势估计中的应用AI 解读

This article describes a simple method of estimating the autocorrelations of survey errors for the case of routing-panel sampling designs. Such designs are in routine use, for example, in labor force surveys (LFS). The survey-error autocorrelations induce spurious trends that need to be separated from the trend of the population values when the latter is of interest. This can be achieved by use of simple state-space models that combine the model holding for the population…

摘要暂缺

摘要暂缺

I consider estimation and seasonal adjustment of population means based on rotating panel surveys carried out at regular time intervals. The analysis uses a dynamic structural model that assumes a decomposition of the mean into a trend-level component and a seasonal component. The model accounts for the correlations between individual panel estimators and for possible rotation group effects. It can be applied to general rotation patterns using either the individual panel…

摘要暂缺

Regression models that account for main state effects and nested county effects are considered for the assessment of farmland values. Empirical predictors obtained by replacing the unknown variances in the formula of the optimal predictors by maximum likelihood estimates are presented. The computations are carried out by simple iterations between two SAS procedures. Estimators for the prediction variances are derived, and a modification to secure the robustness of the…

分层均值与规模未知时利用回归估计量进行后分层AI 解读

Poststratification is often used to increase the precision of survey estimators. When applied to regression estimators, it assumes that the regression relationships in the various strata are different. This gives rise to the use of the separate regression estimator. In this paper, we consider situations where the strata affiliation of the population units is not specified in the frame so that the separate regression estimator is not available, the strata means of the…

抽样设计与响应机制对多元回归预测量的影响AI 解读

A general regression procedure for the prediction of a vector of population means in situations of nonresponse is proposed. The multivariate treatment of the prediction problem is not computationally complicated and allows the borrowing of information from one target variable to the other even when both variables are subject to nonresponse. The predictors are optimal under a general model that specifies the first and second moments of the joint distribution of the survey…

抽样设计与应答机制对多变量回归预测量的影响AI 解读

A general regression procedure for the prediction of a vector of population means in situations of nonresponse is proposed. The multivariate treatment of the prediction problem is not computationally complicated and allows the borrowing of information from one target variable to the other even when both variables are subject to nonresponse. The predictors are optimal under a general model that specifies the first and second moments of the joint distribution of the survey…