Mean, Covariance, and Likelihood
Asymptotic expansions in mean and covariance structure analysis
... Simulations were performed under normality and nonnormality, where the elements of f and e were independently distributed with means and covariances as specified in (5.1). In the nonnormal cases, independently chi-square ...
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Regularized covariance estimation for weighted maximum likelihood policy search methods
... maximum likelihood estimate (WMLE) to update the mean and covariance matrix of this distribution in each ...of covariance matrices and limited number of samples, the WMLE is an unreliable ...
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CiteSeerX — Modelling Mean-Covariance Structures in the Growth Curve Model
... of covariance structures, for example, unstructured covariance (UC) by Potthoff and Roy (1964) and von Rosen (1989), the SCS by Rao (1966) and Lee (1988), compound symmetry structure by Lee (1988), AR(1) by ...
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Joint Variable Selection of Mean Covariance Model for Longitudinal Data
... maximum likelihood estimates and model selection for joint mean-covariance analysis based ...of mean and covariance structures for longitudinal data, that implies that our method can ...
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Alternative Multiple Imputation Inference for Mean and Covariance Structure Modeling
... maximum likelihood (FIML; Anderson, 1957; Arbuckle, 1996) is an often recom- mended alternative estimation strategy for mean and covariance structure model- ing when some observations are ...how ...
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Better Estimates of Genetic Covariance Matrices by “Bending” Using Penalized Maximum Likelihood
... of covariance matrices, in particular for cases where the ratio between the number of observations and the number of variables is ...of covariance matrices have been suggested that counteract bias in ...
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Nonparametric maximum likelihood estimation of the structural mean of a sample of curves
... with covariance matrix explicitly computable from the covariance functional ...maximum likelihood estimator is able to attain the parametric consistency rate n ...
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Linear response methods for accurate covariance estimates from mean field variational bayes
... 3 The likelihood described in Section 3.3 is symmetric under relabeling. When the component locations and shapes have a real-life interpretation, the researcher is generally interested in the uncertainty of µ, Λ, ...
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Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data
... This article is protected by copyright. All rights reserved 2 dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics ...
A linear mixed model, with non-stationary mean and covariance, for soil potassium based on gamma radiometry
... Figure 4 displays a hierarchy of non-stationary variance models that were fitted, with lines connecting models that are nested and therefore for which likelihood-ratio tests can be carried out. P -values are ...
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Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non Normal Random Effects
... the covariance structure plays a crucial role in statistical ...of covariance structure improves the efficiencies of the mean parameters and provides much more reliable estimates (Ye and Pan, 2006, ...
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"Maximum Covariance Di erence Test for Equality of Two Covariance Matrices"
... non-zero mean matrix ...the likelihood ratio test and other omnibus type test procedures are equivalent and have non-central χ 2 distribution with p(p + 1)/2 degrees of freedom and non-centrality parameter ...
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Mean Empirical Likelihood
... Empirical likelihood methods are widely used in different settings to construct the confidence regions for parameters which satisfy the moment ...empirical likelihood ratio confidence regions may have poor ...
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Maximum likelihood estimation of a stochastic frontier model with residual covariance
... Abstract In theoretical literature on productivity, the disturbance terms of the stochastic frontier model are assumed to be independent random variables. In this paper, we consider a stochastic production frontier model ...
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Covariance Tapering for Likelihood Based. Estimation in Large Spatial Datasets
... Maximum likelihood is an attractive method of estimating covariance pa- rameters in spatial models based on Gaussian ...the likelihood can be computationally infeasible for large datasets, requiring ...
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Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
... the covariance graph model induced by the graph G in Figure 5(i) has a deviance of ...maximum likelihood estimate obtained by fitting the model to the correlation matrix is shown in the lower-diagonal part ...
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Maximum likelihood estimation of mean reverting processes
... a mean reverting process starting at a level x(0) = 12, that tends to revert to a level ¯ x = 15, with a speed of reversion η = 4 and a short term standard deviation σ = 5 (one third of the level of ...
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Joint Mean and Covariance Modeling of Matrix-Variate Data
... for mean pa- rameters ...the mean and covariance with a single instance of the data matrix, as is common in ...between mean and covari- ance ...two-way covariance estimation for ...
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Asymptotic Behavior of the Likelihood Function of Covariance Matrices of Spatial Gaussian Processes
... difficult-to-optimize covariance model functions. Ababou et al. 11 show that likelihood-optimized hyperparameters may correspond to ill- conditioned correlation ...of covariance models, ...
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The mean-variance model from the inverse of the variance-covariance matrix
... − (The coefficient c corresponds to the minimum variance among all the portfolios with zero expected return.) This shows that the function relating the expected return t to the variance V along the critical line is a ...
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