Search

Your search keyword '"empirical best linear unbiased predictors"' showing total 4 results
Start Over Descriptor "empirical best linear unbiased predictors"
4 results on '"empirical best linear unbiased predictors"'

1. On prediction of population and subpopulation characteristics for future periods.

Author

Ża̧dło, Tomasz

Subjects
*EMPIRICAL Bayes methods, *PARTICLE size distribution, *SPECIES distribution, *MONTE Carlo method, *SIMULATION methods & models
Abstract

The estimation or prediction of population characteristics based on the sample information is the key issue in survey sampling. If the sample sizes in subpopulations (domains) are large enough, similar methods as used for the whole population can be used to estimate or to predict subpopulations characteristics as well. To estimate or to predict characteristics of domains with small or even zero sample sizes, small area estimation methods “borrowing strength” from other subpopulations or time periods are widely used. We extend this problem and study methods of prediction of future population and subpopulations’ characteristics based on the longitudinal data. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

2. Using a system mixed-effects modeling method to estimate tree compartment biomass for intensively managed loblolly pines—an allometric approach.

Author

Zhang, Yujia and Borders, Bruce E.

Subjects
TREES, BIOMASS, LOBLOLLY pine, FORESTS & forestry
Abstract

Allometric relationships between total and individual tree compartment (stem, root, branch, and foliage) biomass were investigated for intensively managed loblolly pine (Pinus taeda L.) plantations in the Lower Coastal Plain and Piedmont of Georgia. It was found that the percent of stem biomass increases with age while the inverse relationship was found for foliage and branches. Cultural treatments also impacted the proportional allocation among various tree compartments. Specifically, this investigation showed that compared with unfertilized trees, annually fertilized trees in the Lower Coastal Plain had a larger proportional biomass allocation to the stem relative to root, branch, and foliage compartments. A system of related compartment biomass models was developed that reproduces observed relationships in the raw data. This system of models uses total tree biomass to obtain estimates of biomass for other tree compartments. The flexibility of the system of biomass prediction models allows users to estimate biomass for the various compartments as a function of another known compartment biomass. [Copyright &y& Elsevier]

Published
2004
Full Text
View/download PDF

3. Prediction of failure probability of oil wells

Author

João Batista Queiroz de Carvalho, Julio M. Singer, and Dione Maria Valença

Subjects
Statistics and Probability, Mixed model, Empirical Bayes predictors, Gaussian, PROBABILIDADE, Correlated data, Context (language use), Best linear unbiased prediction, Accelerated failure time model, Accelerated failure time models, Random effects model, random effects models, empirical Bayes predictors, Random effects models, symbols.namesake, Bayes' theorem, empirical best linear unbiased predictors, Empirical best linear unbiased predictors, Statistics, symbols, Econometrics, correlated data, Parametric statistics, Mathematics
Abstract

We consider parametric accelerated failure time models with random effects to predict the probability of possibly correlated failures occurring in oil wells. In this context, we first consider empirical Bayes predictors (EBP) based on aWeibull distribution for the failure times and on a Gaussian distribution for the random effects.We also obtain empirical best linear unbiased predictors (EBLUP) using a linear mixed model for which the form of the distribution of the random effects is not specified. We compare both approaches using data obtained from an oil-drilling company and suggest how the results may be employed in designing a preventive maintenance program.

Published
2014

4. O dokładności pewnego predyktora typu EBLU

Author

Żądło, Tomasz and Department of Statistics, University of Economics in Katowice

Subjects
small area estimation, empirical best linear unbiased predictors, general mixed linear model
Abstract

In the paper we analyze the accuracy of the empirical best linear unbiased predictor (EBLUP) of the domain total (see Royall, 1976) assuming a special case of the general linear mixed model. To estimate the mean square error (MSE) of the EBLUP we use the results obtained by Datta and Lahiri (2000) for the predictor proposed by Henderson (1950) and adopt them for the predictor proposed by Royall (1976). In a simulation study we study real data on Polish farms from Dąbrowa Tarnowska region. W opracowaniu analizujemy dokładność empirycznych najlepszych liniowych nieobciążonych predyktorów wartości globalnej w domenie (ang. EBLUP - empirical best linear unbiased predictor) zakładając model nadpopulacji należący do klasy ogólnych mieszanych modeli liniowych. Do oceny błędu średniokwadratowego (ang. MSE - mean square error) predyktora typu EBLU wykorzystano rezultaty prezentowane przez Datta and Lahiri (2000) dla predyktora zaproponowanego przez Hendersona (1950) po zaadoptowaniu ich dla przypadku predyktora zaproponowanego przez Royalla (1976). W badaniu symulacyjnym wykorzystano rzeczywiste dane dotyczące gospodarstw rolnych w powiecie Dąbrowa Tarnowska uzyskane w spisie rolnym w 1996. Zadanie pt. Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej naukę.

Published
2008

Catalog

Books, media, physical & digital resources
See catalog results