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Contributions to Small Area Estimation [Elektronisk resurs] Using Random Effects Growth Curve Model / Innocent Ngaruye

Ngaruye, Innocent, 1973- (författare)
Martin, Singull (preses)
von Rosen, Dietrich (preses)
Nummi, Tapio (opponent)
Linköpings universitet Matematiska institutionen (utgivare)
Alternativt namn: Linköpings universitet. Tekniska högskolan. Matematiska institutionen
Alternativt namn: MAI
Alternativt namn: Linköping University. Department of Mathematics
Linköpings universitet Tekniska fakulteten (utgivare)
Linköping Department of Mathematics, Linköping University, 2017
Engelska 43 s. (PDF) : ill.
Serie: Linköping Studies in Science and Technology. Dissertations 0345-7524 1855
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  • E-bokAvhandling(Diss. (Sammanfattning) Linköping : Linköpings universitet, 2017)
Sammanfattning Ämnesord
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  • This dissertation considers Small Area Estimation with a main focus on estimation and prediction for repeated measures data. The demand of small area statistics is for both cross-sectional and repeated measures data. For instance, small area estimates for repeated measures data may be useful for public policy makers for different purposes such as funds allocation, new educational or health programs, etc, where decision makers might be interested in the trend of estimates for a specic characteristic of interest for a given category of the target population as a basis of their planning. It has been shown that the multivariate approach for model-based methods in small area estimation may achieve substantial improvement over the usual univariate approach. In this work, we consider repeated surveys taken on the same subjects at different time points. The population from which a sample has been drawn is partitioned into several non-overlapping subpopulations and within all subpopulations there is the same number of group units. The aim is to propose a model that borrows strength across small areas and over time with a particular interest of growth profiles over time. The model accounts for repeated surveys, group individuals and random effects variations. Firstly, a multivariate linear model for repeated measures data is formulated under small area estimation settings. The estimation of model parameters is discussed within a likelihood based approach, the prediction of random effects and the prediction of small area means across timepoints, per group units and for all time points are obtained. In particular, as an application of the proposed model, an empirical study is conducted to produce district level estimates of beans in Rwanda during agricultural seasons 2014 which comprise two varieties, bush beans and climbing beans. Secondly, the thesis develops the properties of the proposed estimators and discusses the computation of their first and second moments. Through a method based on parametric bootstrap, these moments are used to estimate the mean-squared errors for the predicted small area means. Finally, a particular case of incomplete multivariate repeated measures data that follow a monotonic sample pattern for small area estimation is studied. By using a conditional likelihood based approach, the estimators of model parameters are derived. The prediction of random effects and predicted small area means are also produced. 

Ämnesord

Stickprovsteori  (sao)
Skattningsteori  (sao)
Natural Sciences  (hsv)
Mathematics  (hsv)
Probability Theory and Statistics  (hsv)
Naturvetenskap  (hsv)
Matematik  (hsv)
Sannolikhetsteori och statistik  (hsv)
Engineering and Technology  (hsv)
Electrical Engineering, Electronic Engineering, Information Engineering  (hsv)
Control Engineering  (hsv)
Teknik och teknologier  (hsv)
Elektroteknik och elektronik  (hsv)
Reglerteknik  (hsv)
Engineering and Technology  (hsv)
Electrical Engineering, Electronic Engineering, Information Engineering  (hsv)
Signal Processing  (hsv)
Teknik och teknologier  (hsv)
Elektroteknik och elektronik  (hsv)
Signalbehandling  (hsv)
Social Sciences  (hsv)
Economics and Business  (hsv)
Samhällsvetenskap  (hsv)
Ekonomi och näringsliv  (hsv)
Natural Sciences  (hsv)
Computer and Information Science  (hsv)
Bioinformatics (Computational Biology)  (hsv)
Naturvetenskap  (hsv)
Data- och informationsvetenskap  (hsv)
Bioinformatik (beräkningsbiologi)  (hsv)
Sampling (Statistics)  (LCSH)

Klassifikation

519.52 (DDC)
62D05 (msc)
Thib (kssb/8 (machine generated))
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