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利用自助法权重检验复杂调查数据假设：统一方法AI 解读

Standard statistical methods without taking proper account of the complexity of a survey design can lead to erroneous inferences when applied to survey data due to unequal selection probabilities, clustering, and other design features. In particular, the Type I error rates of hypotheses tests using standard methods can be much larger than the nominal significance level. Methods incorporating design features in testing hypotheses have been proposed, including Wald tests and…

复杂调查数据的贝叶斯经验似然推断AI 解读

Summary We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically…

基本区域层级模型下小区域估计量的变异性度量AI 解读

Gauri Sankar Datta, J. N. K. Rao, David Daniel Smith; ‘On measuring the variability of small area estimators under a basic area level model’, Biometrika, V

合并两个独立调查的数据：模型辅助方法AI 解读

Combining information from two or more independent surveys is a problem frequently encountered in survey sampling. We consider the case of two independent surveys, where a large sample from survey 1 collects only auxiliary information and a much smaller sample from survey 2 provides information on both the variables of interest and the auxiliary variables. We propose a model-assisted projection method of estimation based on a working model, but the reference distribution is…

多抽样框调查的伪经验似然推断AI 解读

This article presents a pseudo–empirical likelihood approach to inference for multiple-frame surveys. We establish a unified framework for point and interval estimation of finite population parameters, and show that inferences on the parameters of interest making effective use of different types of auxiliary population information can be conveniently carried out through the constrained maximization of the pseudo–empirical likelihood function. Confidence intervals are…

复杂调查的贝叶斯伪经验似然区间AI 解读

Summary Bayesian methods for inference on finite population means and other parameters by using sample survey data face hurdles in all three phases of the inferential procedure: the formulation of a likelihood function, the choice of a prior distribution and the validity of posterior inferences under the design-based frequentist framework. In the case of independent and identically distributed observations, the profile empirical likelihood function of the mean and a…

调查数据项目无应答插补后的线性化方差估计统一方法AI 解读

Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple imputation and composite imputation. Results from a simulation study, under a…

非线性混合模型中小区域预测均方误差的刀切估计AI 解读

Empirical Bayes predictors of small area parameters of interest are often obtained under a linear mixed model for continuous response data or a generalized linear mixed model for binary responses or count data. However, estimation of the unconditional mean squared error of prediction is complicated, particularly for a nonlinear mixed model. Jiang et al. (2002) proposed a jackknife method for estimating the unconditional mean squared error and showed that the resulting…

Summary Imputation is commonly used to compensate for item non-response in sample surveys. If we treat the imputed values as if they are true values, and then compute the variance estimates by using standard methods, such as the jackknife, we can seriously underestimate the true variances. We propose a modified jackknife variance estimator which is defined for any without-replacement unequal probability sampling design in the presence of imputation and non-negligible…

用均值估计方程分析簇大小不可忽略的簇相关数据AI 解读

Most methods for analysing cluster-correlated biological data implicitly assume the ignorability of cluster sizes. When this assumption fails, the resulting inferences may be asymptotically invalid. Hoffman et al. (2001) proposed a simple but computationally intensive method, based on a large number of within-cluster resamples and associated separate estimating equations, that leads to asymptotically valid inferences whether the cluster sizes are ignorable or not. We study a…

基本区域层级模型下小区域估计量的变异性度量AI 解读

In this paper based on a basic area level model we obtain second-order accurate approximations to the mean squared error of model-based small area estimators, using the Fay & Herriot (1979) iterative method of estimating the model variance based on weighted residual sum of squares. We also obtain mean squared error estimators unbiased to second order. Based on simulations, we compare the finite-sample performance of our mean squared error estimators with those based on…

经验预测量的均方误差AI 解读

The term “empirical predictor” refers to a two-stage predictor of a linear combination of fixed and random effects. In the first stage, a predictor is obtained but it involves unknown parameters; thus, in the second stage, the unknown parameters are replaced by their estimators. In this paper, we consider mean squared errors (MSE) of empirical predictors under a general setup, where ML or REML estimators are used for the second stage. We obtain second-order approximation to…

缺失响应数据插补下的经验似然推断AI 解读

Inference under kernel regression imputation for missing response data is considered. An adjusted empirical likelihood approach to inference for the mean of the response variable is developed. A nonparametric version of Wilks' theorem is proved for the adjusted empirical log-likelihood ratio by showing that it has an asymptotic standard chi-squared distribution, and the corresponding empirical likelihood confidence interval for the mean is constructed. With auxiliary…

使用后分层信息的估计量在插补下的刀切方差估计AI 解读

Poststratified estimators are commonly used in sample surveys to improve the efficiency of estimators and to ensure calibration to known poststrata counts. Similarly, generalized regression estimators are used to handle two or more poststratifiers with known marginal counts. In addition, weighting adjustment within weighting classes is used to handle unit nonresponse, and imputation within imputation classes is used to handle item nonresponse. For the full response case,…

插补下使用后分层信息的估计量的刀切方差估计AI 解读

Poststratified estimators are commonly used in sample surveys to improve the efficiency of estimators and to ensure calibration to known poststrata counts. Similarly, generalized regression estimators are used to handle two or more poststratifiers with known marginal counts. In addition, weighting adjustment within weighting classes is used to handle unit nonresponse, and imputation within imputation classes is used to handle item nonresponse. For the full response case,…

双抽样框调查的统计推断AI 解读

In a dual frame survey, samples are drawn independently from two overlapping frames that together cover the population of interest. Several estimators for population totals in dual frame surveys are discussed and compared under a unified setup. We propose jackknife variance estimators, establish their design consistency, and explore their finite-sample performance in a simulation study.

双抽样框调查的统计推断AI 解读

In a dual frame survey, samples are drawn independently from two overlapping frames that together cover the population of interest. Several estimators for population totals in dual frame surveys are discussed and compared under a unified setup. We propose jackknife variance estimators, establish their design consistency, and explore their finite-sample performance in a simulation study.

插补调查数据的方差估计AI 解读

Unit nonresponse and item nonresponse both occur frequently in surveys. Unit nonresponse is customarily handled by weighting adjustment, whereas item nonresponse is usually treated by some form of imputation. In particular, deterministic or stochastic imputation is often used to assign values for missing item responses. We provide an account of some recent work on jackknife variance estimation based on adjusted imputed values, using only a single imputation and hence a…

复杂抽样设计下双抽样框调查的估计AI 解读

In a dual frame survey, samples are drawn independently from two overlapping frames that are assumed to cover the population of interest. This article considers the case when at least one of the samples is selected by a complex design involving, e.g., multistage sampling. A “pseudo”—maximum likelihood estimator of a population total or a mean for such dual frame surveys is proposed. An advantage of the proposed estimator is that the same weights are used for all the…

分层随机抽样中的平衡半样本方差估计AI 解读

Establishment surveys based on list frames often use stratified random sampling with a small number of strata, H, and relatively large sample sizes, n h , within strata. For such surveys, a grouped balanced half-sample (GBHS) method is often used for variance estimation and for construction of confidence intervals on population parameters of interest. In this method the sample in each stratum is first randomly divided into two groups, and then the balanced half-sample (BHS)…

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两阶段抽样的方差估计及其在缺失数据插补中的应用AI 解读

Ratio estimation under two-phase simple random sampling is studied. A new linearisation variance estimator that makes more complete use of the sample data than a standard one is proposed. A jackknife variance estimator and its linearised version are also obtained. Unconditional and conditional repeated sampling properties of these variance estimators are studied through simulation. Applications to ‘mass’ imputation under two-phase sampling and deterministic imputation for…

两阶段抽样下的方差估计及缺失数据插补应用AI 解读

Ratio estimation under two-phase simple random sampling is studied. A new linearisation variance estimator that makes more complete use of the sample data than a standard one is proposed. A jackknife variance estimator and its linearised version are also obtained. Unconditional and conditional repeated sampling properties of these variance estimators are studied through simulation. Applications to ‘mass’ imputation under two-phase sampling and deterministic imputation for…

两阶段整群抽样回归分析中的广义最小二乘 F 检验AI 解读

When data are obtained by two-stage cluster sampling, serious problems can arise if conventional methods that ignore the intracluster correlations are used. Wu, Holt, and Holmes showed that the standard F statistic in regression analysis leads to inflated type I error rate (or size) due to correlated errors in the regression model appropriate for clustered data. They proposed a simple correction to the standard F statistic that takes into account common intracluster…