| 1. |
- Ngaruye, Innocent, et al.
(författare)
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Small area estimation under a multivariate linear model for incomplete repeated measures data
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, the issue of analysis of multivariate repeated measures data that follow a monotonic sample pattern for small area estimation is addressed. Random effects growth curve models with covariates for both complete and incomplete data are formulated. A conditional likelihood based approach is proposed for estimation of the mean parameters and covariances. Further, the prediction of random effects and predicted small area means are also discussed. The proposed techniques may be useful for small area estimation under longitudinal surveys with grouped response units and drop outs.
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| 2. |
- Ngaruye, Innocent, et al.
(författare)
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Small Area Estimation under a Multivariate Linear Model for Repeated Measures Data
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we consider small area estimation under a multivariate linear regression model for repeated measures data. The aim of the proposed model is to get a model which borrows strength across small areas and over time, by incorporating simultaneously the area effects and time correlation. The model accounts for repeated surveys, group individuals and random effects variations. Estimation of model parameters is discussed within a restricted maximum likelihood based approach. Prediction of random e ects and the prediction of small area means across time points and per group units for all time points are derived. The results are supported by a simulation study.
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| 3. |
- Nzabanita, Joseph, et al.
(författare)
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Extended GMANOVA Model with a Linearly Structured Covariance Matrix
- 2015
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Ingår i: Mathematical Methods of Statistics. - : Linköping University Electronic Press. - 1066-5307 .- 1934-8045. ; 24, s. 280-291
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we consider the extended generalized multivariate analysis of variance (GMANOVA) with a linearly structured covariance matrix. The main theme is to find explicit estimators for the mean and for the linearly structured covariance matrix. We show how to decompose the residual space, the orthogonal complement to the mean space, into m + 1 orthogonal subspaces and how to derive explicit estimators of the covariance matrix from the sum of squared residuals obtained by projecting observations on those subspaces. Also an explicit estimator of the mean is derived and some properties of the proposed estimators are studied.
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| 6. |
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| 7. |
- Von Rosen, Dietrich
(författare)
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Maximum likelihood estimation in the tensor normal model with a structured mean
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- There is a growing interest in the analysis of multi-way data. In some studies theinference about the dependencies in three-way data is done using the third order tensornormal model, where the focus is on the estimation of the variance-covariance matrix whichhas a Kronecker product structure. Little attention is paidto the structure of the mean,though, there is a potential to improve the analysis by assuming a structured mean. Inthis paper, we introduce a 2-fold growth curve model by assuming a trilinear structure forthe mean in the tensor normal model and propose an algorithm for estimating parameters.Also, some direct generalizations are presented.
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| 8. |
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| 9. |
- Von Rosen, Dietrich
(författare)
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Recursive formula for E(∏i Tr{(WΣ-1)mi}), where W~Wp(∑; n) in finite and asymptotic regime
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we give a general recursive formula forE[∏ki=0Tr{Wmi}], whereW∼ Wp(I,n) denotes a real Wishart matrix. Formulas for fixed n,pare presented as well as asymptotic versions when npn,p→∞→c, i.e., when the so called Kolmogorov condition holds. Finally, we show application of the asymptotic moment relation when deriving moments for the Marchenko-Pastur distribution (free Poisson law). A numerical illustration using implementation of the main result is also performed
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| 10. |
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