Results 51 to 60 of about 451,504 (196)
Química Nova, 2008 The objective of this study was to develop and validate an analytical method for quantification of glucosamine and chondroitin in pharmaceutical formulations. Multivariate calibration combined with infrared spectrophotometry allowed this analysis.
Paula Rossignoli +4 more
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Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering [PDF]
, 2016 The two main topics of this paper are the introduction of the "optimally tuned improper maximum likelihood estimator" (OTRIMLE) for robust clustering based on the multivariate Gaussian model for clusters, and a comprehensive simulation study comparing ...
Coretto, Pietro, Hennig, Christian
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Projection Pursuit Multivariate Sampling of Parameter Uncertainty
Applied Sciences, 2022 The efficiency of sampling is a critical concern in Monte Carlo analysis, which is frequently used to assess the effect of the uncertainty of the input variables on the uncertainty of the model outputs.
Oktay Erten +2 more
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, 2015 The Cramer-Rao lower bound for the estimation of the affine transformation parameters in a multivariate heteroscedastic errors-in-variables model is derived.
Cohen, E. A. K., Kim, D., Ober, R. J.
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Bayesian Geoadditive Seemingly Unrelated Regression [PDF]
, 2002 Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A
Adebayo, Samson B. +3 more
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A new approach fits multivariate genomic prediction models efficiently
Genetics Selection Evolution, 2022 Background Fast, memory-efficient, and reliable algorithms for estimating genomic estimated breeding values (GEBV) for multiple traits and environments are needed to make timely decisions in breeding.
Alencar Xavier, David Habier
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IATSS Research, 2021 Road safety modeling enables the development of crash prediction models and the investigation of which factors contribute to crash occurrence. Developing multivariate response models is also valuable, but such models are currently under-exploited ...
Philippe Barbosa Silva +2 more
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Maximum-likelihood estimation for diffusion processes via closed-form density expansions [PDF]
, 2013 This paper proposes a widely applicable method of approximate maximum-likelihood estimation for multivariate diffusion process from discretely sampled data.
Li, Chenxu
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Multiple Response Variables Regression Models in R: The mcglm Package
Journal of Statistical Software, 2018 This article describes the R package mcglm implemented for fitting multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling framework for normal and non-normal multivariate data analysis, designed to ...
Wagner Hugo Bonat
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BarekengMulti-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables.
Karin Joan, Robyn Irawan, Benny Yong
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