Improved shrinkage estimators for the mean vector of a scale mixture of normals with unknown variance

1988 ◽  
Vol 16 (3) ◽  
pp. 237-245 ◽  
Author(s):  
Gina Bravo ◽  
Brenda Macgibbon
Keyword(s):  
Shrinkage Estimators ◽  
Scale Mixture ◽  
Unknown Variance ◽  
Mixture Of Normals ◽  
The Mean ◽  
Mean Vector ◽  
Scale Mixture Of Normals

scholarly journals The Location Parameter Estimation of Spherically Distributions with Known Covariance Matrices

2020 ◽  
Vol 8 (2) ◽  
pp. 499-506
Author(s):  
Mahmoud Afshari ◽  
Hamid Karamikabir
Keyword(s):  
Location Parameter ◽  
Covariance Matrices ◽  
Spherically Symmetric ◽  
Shrinkage Estimators ◽  
Parameter Vector ◽  
Symmetric Distributions ◽  
The Mean ◽  
Simulation Results ◽  
Balance Error ◽  
Mean Vector

This paper presents shrinkage estimators of the location parameter vector for spherically symmetric distributions. We suppose that the mean vector is non-negative constraint and the components of diagonal covariance matrix is known.We compared the present estimator with natural estimator by using risk function.We show that when the covariance matrices are known, under the balance error loss function, shrinkage estimator has the smaller risk than the natural estimator. Simulation results are provided to examine the shrinkage estimators.

Tracking maneuvering targets using a scale mixture of normals

2004 ◽  
Author(s):  
Simon Maskell ◽  
Neil J. Gordon ◽  
Nick Everett ◽  
Martin Robinson
Keyword(s):  
Scale Mixture ◽  
Maneuvering Targets ◽  
Mixture Of Normals ◽  
Scale Mixture Of Normals

Tests for the mean vector under intraclass covariance structure

1981 ◽  
Vol 12 (3-4) ◽  
pp. 237-245 ◽  
Author(s):  
Bernard Clement ◽  
Sukharanyan Chakraborty ◽  
Bimal K. Sinha ◽  
Narayan C. Giri
Keyword(s):  
Covariance Structure ◽  
The Mean ◽  
Mean Vector

scholarly journals BAYESIAN INFERENCE FOR THE TANGENT PORTFOLIO

2018 ◽  
Vol 21 (08) ◽  
pp. 1850054 ◽  
Author(s):  
DAVID BAUDER ◽  
TARAS BODNAR ◽  
STEPAN MAZUR ◽  
YAREMA OKHRIN
Keyword(s):  
Bayesian Inference ◽  
Point Of View ◽  
Posterior Distributions ◽  
Conditional Distributions ◽  
Linear Combinations ◽  
Conjugate Priors ◽  
Mean And Variance ◽  
The Mean ◽  
Stochastic Representations ◽  
Mean Vector

In this paper, we consider the estimation of the weights of tangent portfolios from the Bayesian point of view assuming normal conditional distributions of the logarithmic returns. For diffuse and conjugate priors for the mean vector and the covariance matrix, we derive stochastic representations for the posterior distributions of the weights of tangent portfolio and their linear combinations. Separately, we provide the mean and variance of the posterior distributions, which are of key importance for portfolio selection. The analytic results are evaluated within a simulation study, where the precision of coverage intervals is assessed.

Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Wei Zhao ◽  
Wei Hou ◽  
Ramon C. Littell ◽  
Rongling Wu
Keyword(s):  
Genetic Correlations ◽  
Covariance Matrices ◽  
Growth Trajectories ◽  
Relative Importance ◽  
Hybrid Progeny ◽  
Linkage Groups ◽  
Stem Height ◽  
Pleiotropic Qtl ◽  
The Mean ◽  
Mean Vector

In this article, we present a statistical model for mapping quantitative trait loci (QTL) that determine growth trajectories of two correlated traits during ontogenetic development. This model is derived within the maximum likelihood context, incorporated by mathematical aspects of growth processes to model the mean vector and by structured antedependence (SAD) models to approximate time-dependent covariance matrices for longitudinal traits. It provides a quantitative framework for testing the relative importance of two mechanisms, pleiotropy and linkage, in contributing to genetic correlations during ontogeny. This model has been employed to map QTL affecting stem height and diameter growth trajectories in an interspecific hybrid progeny of Populus, leading to the successful discovery of three pleiotropic QTL on different linkage groups. The implications of this model for genetic mapping within a broader context are discussed.

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