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Multilevel Models Allow Modular Specification of What and Where to Regularize, Especially in Small Area Estimation [article]

Michael Tzen
2018 arXiv   pre-print
We conclude by discussing optional criteria to incorporate into a MLM prediction and possible entrypoints for extensions.  ...  Through the lense of multilevel model (MLM) specification and regularization, this is a connect-the-dots introductory summary of Small Area Estimation, e.g. small group prediction informed by a complex  ...  Further, (Fay and Herriot 1979) used a non-informative prior for β but no prior for A i . An Empirical Bayes procedure produced the resulting predictions.  ... 
arXiv:1805.08233v1 fatcat:k5m7mou7vfdalkvjoarbacleja

Small area estimation of non-monetary poverty with geospatial data

Takaaki Masaki, David Newhouse, Ani Rudra Silwal, Adane Bedada, Ryan Engstrom
2022 Statistical Journal of the IAOS  
Using data from Tanzania and Sri Lanka and applying a household-level empirical best (EB) predictor mixed model, we find that combining survey data with geospatial data significantly improves both the  ...  While the EB predictor model moderately underestimates standard errors of those point estimates, coverage rates are similar to standard survey-based standard errors that assume independent outcomes across  ...  We thank Paul Corral, Kristen Himelein, Peter Lanjouw, and William Seitz for insightful comments and suggestions, and Partha Lahiri, Carl Morris and Roy van der Weide for helpful discussions.  ... 
doi:10.3233/sji-210902 fatcat:uw43245elffotegg5elq2y3axu

Estimating the prevalence of anemia rates among children under five in Peruvian districts with a small sample size [article]

Anna Sikov, Jose Cerda-Hernandez
2022 arXiv   pre-print
Specifically, the interest of the present paper is to understand to which extent employing the basic and the spatial Fay-Herriot models can compensate for inadequate sample size in most of the sampled  ...  In this paper we attempt to answer the following question: "Is it possible to obtain reliable estimates for the prevalence of anemia rates in children under five years in the districts of Peru?"  ...  MSEs than the basic Fay-Herriot model.  ... 
arXiv:2208.01593v1 fatcat:buydlulpmbcmbbdn5dlf7vvksu


Marcin Szymkowiak, Andrzej Młodak, Łukasz Wawrowski
2017 Statistics in Transition New Series  
technique -the EBLUP estimator based on the Fay-Herriot model.  ...  The European Survey on Income and Living Conditions (EU-SILC) is the basic source of information published by CSO (the Central Statistical Office of Poland) about the relative poverty indicator, both for  ...  Acknowledgements The authors wish to thank two anonymous reviewers for detailed and helpful comments about the manuscript.  ... 
doi:10.21307/stattrans-2017-003 fatcat:pkkp2ltakfb4df33txhl55z2ei

Small area semiparametric additive monotone models

C Rueda, MJ Lombardía
2012 Statistical Modelling  
A simulation experiment is carried out to compare the performance of the new based-model estimators against the Fay-Herriot approach and to confirm the good performance of the bootstrap.  ...  The semiparametric model-based area estimators are also compared with the parametric based estimators using data on a survey of lakes, where the questions of the prediction of missing data and model selection  ...  Consequently, M 2 would be the selected model if obtaining positive predictions were a priority criteria.  ... 
doi:10.1177/1471082x12465796 fatcat:kfcnpfy2wrelpnwm22j7l6ifmi

Geo-additive Models in Small Area Estimation of Poverty

Novi Hidayat Pusponegoro, Anik Djuraidah, Anwar Fitrianto, I Made Sumertajaya
2019 Journal of Data Science and Its Applications  
The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation  ...  Since, the small area estimation techniques require "borrow strength" across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE.  ...  Akaike's Information Criteria (AIC) is used as information criteria [36] , in order to select the best fit model for the poverty data in Bangka Belitung province despite of the parsimonious model.  ... 
doi:10.21108/jdsa.2019.2.15 fatcat:dov5ab4my5hzbcttba6hkfuoim

A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables

Michael E. Goerndt, Vicente J. Monleon, Hailemariam Temesgen
2011 Canadian Journal of Forest Research  
Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information.  ...  de Fay-Herriot et la prédiction composite basée sur la régression linéaire multiple (PC)).  ...  Acknowledgements We are grateful to Emmor Nile and Dave Enck of the Oregon Department of Forestry for providing both LiDAR and ground data for use in this study.  ... 
doi:10.1139/x11-033 fatcat:pxei6rebsnaebcbkcgrlxq5dcm

Parametric bootstrap methods for bias correction in linear mixed models

Tatsuya Kubokawa, Bui Nagashima
2012 Journal of Multivariate Analysis  
AMS 2010 subject classification: 62F40 62J07 62F12 62F15 Keywords: Best linear unbiased predictor Confidence interval Empirical Bayes procedure Fay-Herriot model Second-order correction Linear mixed model  ...  unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty  ...  This research was supported in part by Grant-in-Aid for Scientific Research Nos. 19200020 and 21540114 from Japan Society for the Promotion of Science.  ... 
doi:10.1016/j.jmva.2011.12.002 fatcat:zsakrd2kd5hotic6gv2udaouza

Robust Small Area Estimation: An Overview

Jiming Jiang, J. Sunil Rao
2020 Annual Review of Statistics and Its Application  
Our discussion also includes topics such as nonparametric SAE methods, Bayesian approaches, model selection and diagnostics, and missing data.  ...  While traditional small area methods and models are widely used nowadays, there have also been much work and interest in robust statistical inference for small area estimation (SAE).  ...  Rao for their comments and discussions that have helped greatly in improving the manuscript.  ... 
doi:10.1146/annurev-statistics-031219-041212 fatcat:kwygx42etnga7hnnnvg6ti24fu

Effective transformation-based variable selection under two-fold subarea models in small area estimation

Song Cai, J. N. K. Rao, Laura Dumitrescu, Golshid Chatrchi
2020 Statistics in Transition New Series  
We present a simple yet effective variable selection method for the two-fold nested subarea model, which generalizes the widely-used Fay-Herriot area model.  ...  The proposed method is motivated by the variable selection method of Lahiri and Suntornchost (2015) for the Fay-Herriot model and the variable selection method of Li and Lahiri (2019) for the unit-level  ...  The celebrated Fay-Herriot (FH) area model (Fay and Herriot, 1979) combines direct estimators and auxiliary variables using a linking model to obtain accurate estimates of small area parameters.  ... 
doi:10.21307/stattrans-2020-031 doaj:d07f6bca9adc43628bb8138cbfebb504 fatcat:xzxnbbrocbbr3ewrxvuphjed2e


Jan A. van den Brakel, Bart Buelens
2015 Statistics in Transition New Series  
This is achieved through conducting covariate selection simultaneously for all editions, minimizing the average of the edition-specific conditional Akaike Information Criteria.  ...  An approach to model selection is proposed resulting in a single set of covariates for multiple survey editions.  ...  Recently, Lahiri and Suntornchost (2015) proposed a new variable selection criterion specifically for Fay-Herriot models.  ... 
doi:10.21307/stattrans-2015-031 fatcat:xubh656jkvhztia2kj7p67nkae

Small area estimation: its evolution in five decades

Malay Ghosh
2020 Statistics in Transition New Series  
The basic papers such as the ones by Fay and Herriott (1979) and Battese, Harter and Fuller (1988) and their follow-ups are discussed in some details. Some of the current topics are also discussed.  ...  The author gratefully acknowledges the Hansen Lecture Committee for their selection.  ...  Acknowledgements I am indebted to Danny Pfeffermann for his line by line reading of the manuscript and making many helpful suggestions, which improved an earlier version of the paper.  ... 
doi:10.21307/stattrans-2020-022 doaj:5f984fa7c1244992ae44e0e4731f8761 fatcat:s47xurgq3jgfpi6xxqmreivcau

A unified Monte-Carlo jackknife for small area estimation after model selection

Jiming Jiang, P. Lahiri, Thuan Nguyen
2018 Annals of Mathematical Sciences and Applications  
We consider estimation of measure of uncertainty in small area estimation (SAE) when a procedure of model selection is involved prior to the estimation.  ...  We prove the second-order unbiasedness of McJack, and demonstrate the performance of McJack in assessing uncertainty in SAE after model selection through empirical investigations that include simulation  ...  in the Fay-Herriot model ( 2 ).  ... 
doi:10.4310/amsa.2018.v3.n2.a2 fatcat:nigtv26ku5chnaezbtcmfrnhw4

A Unified Monte-Carlo Jackknife for Small Area Estimation after Model Selection [article]

Jiming Jiang, P. Lahiri, Thuan Nguyen
2016 arXiv   pre-print
We consider estimation of measure of uncertainty in small area estimation (SAE) when a procedure of model selection is involved prior to the estimation.  ...  We prove the second-order unbiasedness of McJack, and demonstrate the performance of McJack in assessing uncertainty in SAE after model selection through empirical investigations that include simulation  ...  For example, the information criteria, such as AIC (Akaike 1973) and BIC (Schwarz 1978), or the fence methods (see Jiang 2014 for a review), select models from a discrete space of candidate models.  ... 
arXiv:1602.05238v1 fatcat:fzm46l6cozcvbpttks7dk5jzya

Model Selection in Linear Mixed Models

Samuel Müller, J. L. Scealy, A. H. Welsh
2013 Statistical Science  
We arrange, implement, discuss and compare model selection methods based on four major approaches: information criteria such as AIC or BIC, shrinkage methods based on penalized loss functions such as LASSO  ...  Over the last 5-10 years the literature on model selection in linear mixed models has grown extremely rapidly.  ...  We thank two referees and an Associate Editor for their reviews which have lead to an improved paper.  ... 
doi:10.1214/12-sts410 fatcat:x76okbteqfahdk7y4ipoxetdge
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