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On conditional prediction errors in mixed models with application to small area estimation

Shonosuke Sugasawa, Tatsuya Kubokawa
2016 Journal of Multivariate Analysis  
The empirical Bayes estimators in mixed models are useful for small area estimation in the sense of increasing precision of prediction for small area means, and one wants to know the prediction errors  ...  This paper is concerned with conditional prediction errors in the mixed models instead of conventional unconditional prediction errors.  ...  Acknowledgments The second author acknowledges support from Grant-in-Aid for Scientific Research (23243039 and 26330036), Japan.  ... 
doi:10.1016/j.jmva.2016.02.009 fatcat:uw7ucvnfvrguboxjq2olayqin4

Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh

Ying Han
2020 Statistics in Transition New Series  
Unit-level models such as general linear model with correlated sampling errors within small areas, general linear mixed model with nested errors can all be considered.  ...  In this case, the administrative records can rarely be used for unit-level small area estimation model. This limits the application of small area estimation.  ... 
doi:10.21307/stattrans-2020-024 fatcat:436rllohdfflbhnpn6wxhmabvy

Likelihood inference in small area estimation by combining time-series and cross-sectional data

Mahmoud Torabi, Farhad Shokoohi
2012 Journal of Multivariate Analysis  
Using both time-series and cross-sectional data, a linear model incorporating autocorrelated random effects and sampling errors was previously proposed in small area estimation.  ...  However, in practice there are many situations that we have time-related counts or proportions in small area estimation; for example a monthly dataset on the number of incidences in small areas.  ...  This study is based in part on data provided by Manitoba Health through the Manitoba Centre for Health Policy.  ... 
doi:10.1016/j.jmva.2012.05.016 fatcat:vxjjzrtnc5fhhiqqozn4r3qvk4

New Important Developments in Small Area Estimation

Danny Pfeffermann
2013 Statistical Science  
The purpose of this paper is to review and discuss some of the new important developments in small area estimation methods.  ...  The problem of small area estimation (SAE) is how to produce reliable estimates of characteristics of interest such as means, counts, quantiles, etc., for areas or domains for which only small samples  ...  Model-based methods on the other hand usually condition on the selected sample and the inference is with respect to the underlying model.  ... 
doi:10.1214/12-sts395 fatcat:zujvaz76lne7hjy6xbjwv4gxuq

Small area estimation via M-quantile geographically weighted regression

N. Salvati, N. Tzavidis, M. Pratesi, R. Chambers
2010 Test (Madrid)  
One popular approach to small area estimation when data are spatially correlated is to employ Simultaneous Autoregressive (SAR) random effects models to define the Spatial Empirical Best Linear Unbiased  ...  The paper concludes with an application to environmental data for predicting average levels of the Acid Neutralizing Capacity at 8-digit Hydrologic Unit Code level in the Northeast states of the U.S.A.  ...  The authors are grateful to the Space-Time Aquatic Resources Modelling and Analysis Program (STARMAP) for providing access to the data used in this paper.  ... 
doi:10.1007/s11749-010-0231-1 fatcat:eblg4fyumffrzowckitibaxis4

Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh

Isabel Molina
2020 Statistics in Transition New Series  
In the EB procedure by MR, as in typical small area applications with unit level models, the random effects in the nested error model are for the areas of interest.  ...  I would like to extend further on one of the important applications of unit level models that is mentioned in the paper, which is the estimation of poverty or inequality indicators in small areas.  ...  In the EB procedure by MR, as in typical small area applications with unit level models, the random effects in the nested error model are for the areas of interest.  ... 
doi:10.21307/stattrans-2020-026 fatcat:3ocen7x4ubgc3ptxcxxvaeo6ui

Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models

Snigdhansu Chatterjee, Partha Lahiri, Huilin Li
2008 Annals of Statistics  
This method is particularly useful in small area problems.  ...  Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information.  ...  In many small area applications, mixed linear models are now routinely used in combining information from various sources and explaining different sources of errors.  ... 
doi:10.1214/07-aos512 fatcat:dcjpogz7bnethbpqji5qhliedu

GENERALIZED LINEAR MIXED MODELS - AN OVERVIEW

W. W. Stroup, S. D. Kachman
1994 Conference on Applied Statistics in Agriculture  
Recently, through a variety of efforts in a number of contexts, a general framework for generalized linear models with random effects, the "generalized linear mixed model," has been developed.  ...  However, many agricultural research applications lead to mixed or random effects models: split-plot experiments, animal-and plant-breeding studies, multi-location studies, etc.  ...  The rationale for specific estimable or predictable functions is identical to that used in section 3 for the mixed model.  ... 
doi:10.4148/2475-7772.1351 fatcat:qq22yqssivclzbsdcxziqc3wcu

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Justin E. Bagley, Seongeun Jeong, Xinguang Cui, Sally Newman, Jingsong Zhang, Chad Priest, Mixtli Campos-Pineda, Arlyn E. Andrews, Laura Bianco, Matthew Lloyd, Neil Lareau, Craig Clements (+1 others)
2017 Journal of Geophysical Research - Atmospheres  
We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios.  ...  Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model.  ...  Eluszkiewicz, and Thomas Nehrkorn for sharing the STILT code and advice; Paul Novelli for CO measurements used to estimate the CO background;  ... 
doi:10.1002/2016jd025361 fatcat:q6u7bjak3ngrvn2mqhsmlviqjy

Robust Hierarchical Bayes Small Area Estimation for Nested Error Regression Model [article]

Adrijo Chakraborty, Gauri Sankar Datta, Abhyuday Mandal
2018 arXiv   pre-print
Outliers adversely influence standard model-based small area estimates. To deal with outliers, Sinha and Rao (2009) proposed a robust frequentist approach.  ...  Model-based small area estimation is now extensively used to generate reliable statistics by "borrowing strength" from other areas and related variables through suitable models.  ...  M-quantile Small Area Estimation Small area estimation is dominated by linear mixed effects models where the conditional mean of Y ij , the response of the jth unit in the ith small area, is expressed  ... 
arXiv:1702.05832v2 fatcat:rdhaweyavjbufexfxoohzol2cq

INFERENTIAL ISSUES IN MODEL-BASED SMALL AREA ESTIMATION: SOME NEW DEVELOPMENTS

J. N. K. Rao
2015 Statistics in Transition New Series  
We also briefly mention some new work on synthetic estimation of area means or totals based on implicit models.  ...  Small area estimation (SAE) has seen a rapid growth over the past 10 years or so. Earlier work is covered in the author's book (Rao 2003) .  ...  in small area estimation.  ... 
doi:10.21307/stattrans-2015-029 fatcat:koz2r6rwnje3zbqygd2mlennoe

Choosing between AR(1) and VAR(1) models in typical psychological applications

Fabian Dablander, Oisín Ryan, Jonas M. B. Haslbeck, Miguel Angel Sánchez Granero
2020 PLoS ONE  
This allows us to (1) directly investigate the relative performance of AR and VAR models in typical psychological applications, (2) show how the relative performance depends both on n and characteristics  ...  However, the number of observations in typical psychological applications is often small, which puts the reliability of VAR coefficients into question.  ...  Acknowledgments We would like to thank Don van den Bergh, Riet van Bork, Denny Borsboom, Max Hinne, Lourens Waldorp, and two anonymous reviewers for their helpful comments on earlier versions of this paper  ... 
doi:10.1371/journal.pone.0240730 pmid:33119716 fatcat:lzhkdq6srnfjzf7otp2i5jrkxa

Predicting past and future diameter growth for trees in the northeastern United States

James A Westfall
2006 Canadian Journal of Forest Research  
Tree diameter growth models are widely used in forestry applications, often to predict tree size at a future point in time. Also, there are instances where projections of past diameters are needed.  ...  Correlated observations were accounted for via a mixed-effects modeling approach, and an error function was specified to address heterogeneous variance.  ...  The work described above is consistent with most diameter-growth research, where the primary application is to predict future growth based on current conditions.  ... 
doi:10.1139/x06-045 fatcat:ixcnnipcrrhhxpygeii5cjgkmy

Page 8012 of Mathematical Reviews Vol. , Issue 98M [page]

1998 Mathematical Reviews  
of prediction in generalized linear mixed models.  ...  Summary: “Bayesian methods have been used quite extensively in recent years for solving small-area estimation problems.  ... 

Improved EBLUPs in Mixed-Effects Regression Models

Sam Weerahandi
2018 Biostatistics and Biometrics Open Access Journal  
The BLUP in mixed models is a function of the variance components, which are typically estimated by MLE based method.  ...  Therefore, the primary purpose of this paper is to overcome such drawbacks by developing a simple method for the widely used Mixed Models in a regression setting with a number of group structures among  ...  Acknowledgment The authors would like to thank Professor Charles McCulloch for checking the Lemma of the paper to see if there are any obvious errors.  ... 
doi:10.19080/bboaj.2018.04.555641 fatcat:m2b6d6o3mvcudm5rkxmd6ga2b4
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