Robust Models in Probability Sampling

DOI10.1111/1467-9868.00105zbMath0910.62009OpenAlexW2097672871MaRDI QIDQ4214220

David Firth, K. E. Bennett

Publication date: 22 April 1999

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9868.00105

zbMATH Keywords

instrumental variablesmoothingimportance samplingsplinefinite populationlogistic regressiongeneralized linear modelcontrol variatelocal likelihoodauditingbias calibrationbootstrap accelerationstratification survey sampling

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05)

Related Items (13)

Statistical paradises and paradoxes in big data. I: Law of large populations, big data paradox, and the 2016 US presidential election ⋮ ESTIMATION PROCEDURES FOR CATEGORICAL SURVEY DATA WITH NONIGNORABLE NONRESPONSE ⋮ A comparative study of doubly robust estimators of the mean with missing data ⋮ Extensions of the Penalized Spline of Propensity Prediction Method of Imputation ⋮ Mass imputation for two-phase sampling ⋮ The Generalized Oaxaca-Blinder Estimator ⋮ Model-assisted estimation in high-dimensional settings for survey data ⋮ Model-Assisted Estimation Through Random Forests in Finite Population Sampling ⋮ On improved estimation for importance sampling ⋮ Kernel regression estimators for nonparametric model calibration in survey sampling ⋮ A Theory of Statistical Models for Monte Carlo Integration ⋮ Causal inference: a missing data perspective ⋮ Comment: Struggles with survey weighting and regression modeling

This page was built for publication: Robust Models in Probability Sampling