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利用估计函数分析复杂调查数据AI 解读

We describe a method for point and interval estimation of population parameters from complex surveys using estimating functions. The theory was originally developed for infinite populations and has recently been applied to finite populations. With estimating functions, a unifying framework can be given for point and interval estimation of both finite and infinite population parameters. We discuss test inversion methods to derive confidence intervals for one-dimensional…

利用估计函数对复杂调查进行估计AI 解读

We describe a method for point and interval estimation of population parameters from complex surveys using estimating functions. The theory was originally developed for infinite populations and has recently been applied to finite populations. With estimating functions, a unifying framework can be given for point and interval estimation of both finite and infinite population parameters. We discuss test inversion methods to derive confidence intervals for one-dimensional…

The proportional hazards model makes a number of assumptions which may not always be completely satisfied. However, fitting such models can have both descriptive and analytical value. We discuss the implications of the survey design for large-scale studies. We propose a weighted estimator and derive a design-based estimator for the variance of the estimated parameters. The derivation requires using methods not previously used in the context of analysis of survey data. We…

有限总体样本的非参数贝叶斯模型AI 解读

Summary Using Ferguson's (1973) non-parametric priors in a Bayesian analysis of finite populations, we show that asymptotically, at least, the usual estimates and confidence intervals for the population mean in simple and stratified random samples can be justified in Bayesian terms. We then apply these models for estimating population percentiles, and a new procedure for interval estimates in stratified sampling is developed.