Journal ArticleDOI
Post-Stratification: A Modeler's Perspective
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This article developed Bayesian model-based theory for post-stratification, which is a common technique in survey analysis for incorporating population distributions of variables into survey estimates, such as functions of means and totals.Abstract:
Post-stratification is a common technique in survey analysis for incorporating population distributions of variables into survey estimates. The basic technique divides the sample into post-strata, and computes a post-stratification weight w ih = rP h /r h for each sample case in post-stratum h, where r h is the number of survey respondents in post-stratum h, P h is the population proportion from a census, and r is the respondent sample size. Survey estimates, such as functions of means and totals, then weight cases by w h . Variants and extensions of the method include truncation of the weights to avoid excessive variability and raking to a set of two or more univariate marginal distributions. Literature on post-stratification is limited and has mainly taken the randomization (or design-based) perspective, where inference is based on the sampling distribution with population values held fixed. This article develops Bayesian model-based theory for the method. A basic normal post-stratification mod...read more
Citations
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Journal ArticleDOI
How representative is the ACTIVE sample? A statistical comparison of the ACTIVE sample and the HRS sample.
John J. Prindle,John J. McArdle +1 more
TL;DR: The ACTIVE sample is younger, more likely to be female, Black, and more highly educated than the HRS sample, and all results of ACTIVE analyses can be said to be nationally representative based on HRS demographics.
Journal ArticleDOI
Bayes-raking: Bayesian Finite Population Inference with Known Margins
Yajuan Si,Peigen Zhou +1 more
TL;DR: The proposed Bayesian paradigm for raking is applied to the longitudinal study of well-being study and it is demonstrated that model-based approaches significantly improve inferential reliability and substantive findings as a unified survey inference framework.
Journal ArticleDOI
Ecosystem services and justice of protected areas: the case of Circeo National Park, Italy.
TL;DR: In this paper, conservation initiatives are still often struggling to accommodate people's needs, provoking conflicts, and lack of lack of resources, which can cause conflicts and jeopardize the preservation of biodiversity.
References
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The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
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Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
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On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known
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Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
TL;DR: In this paper, three types of Bayesianly justifiable and relevant frequency calculations are presented using examples to convey their use for the applied statistician, and they are discussed in detail.
On the Two Different Aspects of the Representative Method: the Method of Stratified Sampling and the Method of Purposive Selection
TL;DR: The representative method has attracted the attention of many statisticians in different countries as discussed by the authors, mainly due to the general crisis, to the scarcity of money and to the necessity of carrying out statistical investigations connected with social life in a somewhat hasty way.