论文检索

摘要暂缺

调查数据的有限信息似然分析AI 解读

Summary Analysts of survey data are often interested in modelling the population process, or superpopulation, that gave rise to a ‘target’ set of survey variables. An important tool for this is maximum likelihood estimation. A survey is said to provide limited information for such inference if data used in the design of the survey are unavailable to the analyst. In this circumstance, sample inclusion probabilities, which are typically available, provide information which…

双重抽样中回归估计量的方差估计札记AI 解读

Double sampling of a finite population occurs when a sample from the population is itself sampled, with the intent of measuring varites in the subsample not already available in the sample. An important example is the regression estimator for means or totals, which uses values of an auxiliary variable from the full sample to estimate the mean of a variable of interest that is available only on the subsample. This article concerns estimation of the variance of the regression…

利用非参数回归估计有限总体分布函数AI 解读

This paper considers estimators of the distribution function of a variable over a finite population, when a sample of units is available and values of a related auxiliary variable are known for the whole population. Theory is offered for several estimators which rely on nonparametric regression, and for calibration estimators which require a parametric model.

利用非参数校准进行有限总体偏差稳健估计AI 解读

A Standard problem in sample survey inference is that of predicting the finite population total H of a function h(y) of a random variable Y. The model-based approach to this problem first defines a working model ξ for Y and then predicts H by estimating its expectation under ξ, conditional on the sample values of Y. This approach leads to biased predictions if ξ is incorrect. We explore an automatic solution to this misspecification bias that uses nonparametric regression to…

有限总体分布函数估计量的性质AI 解读

Two leading estimators of the finite population distribution function are studied. Their large-sample mean squared errors are compared, under a given model. Neither estimator is universally better; circumstances are identified where the model-based estimator would do poorly, despite employing the correct model.

有限总体分布函数估计量的性质AI 解读

Two leading estimators of the finite population distribution function are studied. Their large-sample mean squared errors are compared, under a given model. Neither estimator is universally better; circumstances are identified where the model-based estimator would do poorly, despite employing the correct model.

通过再杠杆调整构造异方差线性模型的可靠置信区间AI 解读

SUMMARY Under heteroscedasticity, ordinary least squares regression can fail to yield adequate inference on parameter coefficients with respect to hypothesis testing or confidence intervals. For example, confidence intervals can have coverage well below that claimed. This is especially the case in small or moderate-sized imbalanced samples and holds even if the ordinary least squares variance estimator is replaced by a robust-against-heteroscedasticity variance estimator. A…