Analysis

All (2) ((2 results))

• 1. Bayesian hierarchical weighting adjustment and survey inference

  Articles and reports: 12-001-X202000200003

  Description:

  We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.

  Release date: 2020-12-15

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• 2. Postratification into many categories using hierarchical logistic regression Archived

  Articles and reports: 12-001-X19970023616

  Description:

  A standard method for correcting for unequal sampling probabilities and nonresponse in sample surveys is poststratification: that is, dividing the population into several categories, estimating the distribution of responses in each category, and then counting each category in proportion to its size in the population. We consider poststratification as a general framework that includes many weighting schemes used in survey analysis (see Little 1993). We construct a hierarchical logistic regression model for the mean of a binary response variable conditional on poststratification cells. The hierarchical model allows us to fit many more cells than is possible using classical methods, and thus to include much more population-level information, while at the same time including all the information used in standard survey sampling inferences. We are thus combining the modeling approach often used in small-area estimation with the population information used in poststratification. We apply the method to a set of U.S. pre-election polls, poststratified by state as well as the usual demographic variables. We evaluate the models graphically by comparing to state-level election outcomes.

  Release date: 1998-03-12

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Articles and reports (2) ((2 results))

• 1. Bayesian hierarchical weighting adjustment and survey inference

  Articles and reports: 12-001-X202000200003

  Description:

  We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.

  Release date: 2020-12-15

  ---

• 2. Postratification into many categories using hierarchical logistic regression Archived

  Articles and reports: 12-001-X19970023616

  Description:

  A standard method for correcting for unequal sampling probabilities and nonresponse in sample surveys is poststratification: that is, dividing the population into several categories, estimating the distribution of responses in each category, and then counting each category in proportion to its size in the population. We consider poststratification as a general framework that includes many weighting schemes used in survey analysis (see Little 1993). We construct a hierarchical logistic regression model for the mean of a binary response variable conditional on poststratification cells. The hierarchical model allows us to fit many more cells than is possible using classical methods, and thus to include much more population-level information, while at the same time including all the information used in standard survey sampling inferences. We are thus combining the modeling approach often used in small-area estimation with the population information used in poststratification. We apply the method to a set of U.S. pre-election polls, poststratified by state as well as the usual demographic variables. We evaluate the models graphically by comparing to state-level election outcomes.

  Release date: 1998-03-12

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