Variance components models for the covariance structure
MM Algorithms for Variance Components Models.
... correlation structure will lead to invalid standard ...the variance of different structures. In this scenario, variance compomemts model comes into ...
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Covariance models for RNA structure prediction
... crystallographic structure has been reported, ruling out from the analysis the struc- tures annotated as interacting double ...quality structure in the data set has been solved with resolution ...
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Variance Components of Models of Sudoku Square Design
... Variance components are different sources to the variation in an observation and are commonly estimated in the course of determining sampling design, establishing quality control procedure or in statistical ...
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Genetic variance and covariance components for across population evaluation of Brown Swiss cattle
... the variance for national PGM, international PGM and DGV was ...population-specific models and ...The variance of international PGM were on average 5% larger than the variance of national ...
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General Variance Covariance Structures in Two Way Random Effects Models
... The paper is organised as follows. Section 2 shows why Cholesky-type transformations do not work in the two-way EC model. In Section 3 we derive the new “spectral-spectral” combination result for general struc- tures of ...
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Simulation Studies on Estimation of Variance Components for Multilevel Models
... By assuming that the bias of the estimator with respect to the original sample is close to the bias of the estimator of the resamples of the artificial population, we study a procedur[r] ...
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Unconstrained models for the covariance structure of multivariate longitudinal data
... the covariance structure, we take the data’s mean vector to be ...the covariance matrix to be unstructured as ...the variance instead of modeling it, but there is little reason to do so since ...
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On the specification of prior distributions for variance components in disease mapping models
... and structure of the variability of the disease under ...mapping models; we illustrated only one possible specification strategy and others could be ...different models, especially those based on a ...
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Testing multiple variance components in linear mixed-effects models
... multiple variance components, especially for testing a subset of ...the variance least square estimator of variance ...multiple variance components and any subset of them in ...
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Adjusted variance components for unbalanced clustered binary data models
... Unbalanced Clustered Binary Data Models An unequal cluster size with a binary outcome is common in many areas of application, especially in medical research. Sample size formulas for cluster ran- domized trials ...
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Confidence intervals for intraclass correlation coefficients in variance components models
... linear models due to the complexity of bootstrapping variance components, the complexity of multiple clusters, and the diversity of bootstrap ...on variance components has been ...
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On non-negative estimation of variance components in mixed linear models
... Conclusions In this article, two new estimators based on IAUE principle are introduced for estimating the variance components in the mixed linear model. The aim of this article was to evaluate the ...
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Search for the Optimum Variance Components Estimates in Mixed Effects Models
... Minimum Variance (MINVAR) quadratic estimators 20 , the Minimum Mean Square (MIMS) quadratic estimators 16 ...estimated variance components under certain distributional assumptions, the explicit ...
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Using information criteria to select the correct variance-covariance structure for longitudinal data in ecology
... correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated ...
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Formative Measurement Models in Covariance Structure Analysis: Specification and Identification
... measurement models has begun to alter the researchers’ decisions on the statistical method to be used for the estimation of structural equation models with latent variables ...– covariance ...
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Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure
... graphical models and a sparse matrix approximation method using log-determinant ...desired structure of our MR model—sparse interscale graphical structure and sparse in-scale conditional ...
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Spatio-temporal circular models with non-separable covariance structure
... linear models (Harrison and Kanji, 1988; Fisher, 1996; Kato and Shimizu, 2008), linear models in a Bayesian context (Guttorp and Lockhart, 1988; Damien and Walker, 1999), models for circular time ...
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Modelling covariance structure in bivariate marginal models for longitudinal data
... the covariance matrices for multivariate longitudinal data than for univariate case because of the correlations between responses arising from multiple variables and repeated measurements over ...complicated ...
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A Kernel Technique for Forecasting the Variance-Covariance Matrix
... realized covariance matrices can improve forecasts of volatility, based on economic performance, compared to methods using daily ...forecasting models, which have proliferated in number in recent ...recent ...
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Dynamic covariance models
... Dynamic Covariance Models ∗ Ziqi Chen † and Chenlei Leng ‡ Abstract An important problem in contemporary statistics is to understand the relationship among a large number of variables based on a dataset, ...
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