A General Statistical Framework for Multistage Designs

DOI10.1111/j.1467-9469.2011.00745.xzbMath1246.62014OpenAlexW2097324468MaRDI QIDQ2911708

Maria Grünewald, Ola G. Hössjer

Publication date: 1 September 2012

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9469.2011.00745.x

zbMATH Keywords

Fisher informationcost-efficiencymultistage samplingefficient designhierarchical multistage model

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Graphical methods in statistics (62A09)

Related Items (1)

Robin Hood: a cost-efficient two-stage approach to large-scale simultaneous inference with non-homogeneous sparse effects

Uses Software

• R

Cites Work

• Longitudinal data analysis using generalized linear models
• On the Breslow-Holubkov estimator
• Multistage sampling for latent variable models
• Functional approach to optimal experimental design.
• Efficient ascertainment schemes for maximum likelihood estimation
• Ignorability and coarse data
• Auxiliary outcome data and the mean score method
• Large sample theory for semiparametric regression models with two-phase, outcome dependent sampling.
• Approximation Theorems of Mathematical Statistics
• Logistic disease incidence models and case-control studies
• Inference and missing data
• Retrospective studies and failure time models
• Fitting regression models to case-control data by maximum likelihood
• A Note on the Efficiency of Sandwich Covariance Matrix Estimation
• A mean score method for missing and auxiliary covariate data in regression models
• Weighted Average Importance Sampling and Defensive Mixture Distributions
• Weighted Likelihood for Semiparametric Models and Two‐phase Stratified Samples, with Application to Cox Regression
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