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"The missing information principle ‒ A paradigm for analysis of messy s" by Raymond L. Chambers

Sample surveys, as a tool for policy development and evaluation and for scientific, social and economic research, have been employed for over a century. In that time, they have primarily served as tools for collecting data for enumerative purposes. Estimation of these characteristics has been typically based on weighting and repeated sampling, or design-based, inference. However, sample data have also been used for modelling the unobservable processes that gave rise to the finite population data...
Missing Information Principle Combined Data Nformative Sampling Maximum Likelihood Ondeterministic Linkage Small Area Estimation
Source: uow.edu.au

Enhancing Statistical Capacity through the Global Training on Small Area Estimation: Lessons from Asia Pacific, Africa, Latin America and the Caribbean

Conoce los detalles del evento Enhancing Statistical Capacity through the Global Training on Small Area Estimation: Lessons from Asia Pacific, Africa, Latin America and the Caribbean. Accede a toda la información aquí.
United States Asia Pacific Un Statistical Commission Small Area Estimation Global Training Latin America
Source: cepal.org

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"Outlier robust small domain estimation via bias correction and robust " by G. Bertarelli, R. Chambers et al.

Abstract Several methods have been devised to mitigate the effects of outlier values on survey estimates. If outliers are a concern for estimation of population quantities, it is even more necessary to pay attention to them in a small area estimation (SAE) context,where sample size is usually very small and the estimation in often model based. In this paper we set two goals: The first is to review recent developments in outlier robust SAE. In particular, we focus on the use of partial bias corr...
M Quantile Regression Esampling Methods Robust Estimation Small Area Estimation Urvey Sampling Theory
Source: uow.edu.au

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