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Model-based Methods of Classification: Using the mclust Software in Chemometrics [PDF]

open access: yesJournal of Statistical Software, 2007
Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a
Chris Fraley, Adrian E. Raftery
doaj   +1 more source

Risk management via contemporaneous and temporal dependence structures with applications

open access: yesMethodsX, 2021
This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG ...
Emmanuel Senyo Fianu   +2 more
doaj   +1 more source

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study. [PDF]

open access: yesPLoS ONE, 2015
In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between ...
Binod Neupane, Joseph Beyene
doaj   +1 more source

Estimation of a Matrix of Heterogeneity Parameters in Multivariate Meta-Analysis of Random-Effects Models [PDF]

open access: yesJournal of Statistical Theory and Applications (JSTA), 2014
Multivariate meta-analysis has potential over its univariate counterpart. The most common challenge in univariate or multivariate meta-analysis is estimating heterogeneity parameters in non-negative domains under the random-effects model assumption.
Abera Wouhib
doaj   +1 more source

Inclusion of Dominance Effects in the Multivariate GBLUP Model. [PDF]

open access: yesPLoS ONE, 2016
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction
Jhonathan Pedroso Rigal dos Santos   +4 more
doaj   +1 more source

Improved Large Covariance Matrix Estimation Based on Efficient Convex Combination and Its Application in Portfolio Optimization

open access: yesMathematics, 2022
The estimation of the covariance matrix is an important topic in the field of multivariate statistical analysis. In this paper, we propose a new estimator, which is a convex combination of the linear shrinkage estimation and the rotation-invariant ...
Yan Zhang   +3 more
doaj   +1 more source

Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model

open access: yesMathematics, 2022
Efficiency mapping has an important place in examining the maximum efficiency distribution as well as the energy consumption of designed electric motors at maximum torque and speed.
Oğuz Mısır, Mehmet Akar
doaj   +1 more source

Efficient Density Estimation for High-Dimensional Data

open access: yesIEEE Access, 2022
Multivariate density estimation methods typically work well in low dimensions and their extension to data analytics in high dimensions domain has proven challenging. For density estimation in high-dimensional big data domains, the non-parametric Bayesian
Aref Majdara, Saeid Nooshabadi
doaj   +1 more source

A multivariate analysis to propose linear models for the stature estimation in the Sabahan young adult population.

open access: yesPLoS ONE, 2022
BackgroundStature is one of the significant parameters to confirm a biological profile besides sex, age, and ancestry. Sabah is in the Eastern part of Malaysia and is populated by multi-ethnic groups.
Hasanur Bin Khazri   +2 more
doaj   +1 more source

Combining Variable Selection and Multiple Linear Regression for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR Spectroscopy

open access: yesAgronomy, 2022
The successful estimation of soil organic matter (SOM) and soil total nitrogen (TN) contents with mid-infrared (MIR) reflectance spectroscopy depends on selecting appropriate variable selection techniques and multivariate methods for regression analysis.
Hong Li   +5 more
doaj   +1 more source

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