Results 291 to 300 of about 2,907,167 (317)
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Kronecker Factorization-Based Multinomial Logistic Regression for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2022Multinomial logistic regression (MLR) has become prevailing for supervised learning within hyperspectral images (HSIs) community. It seeks the optimal regressors with the given training data.
Xiaotao Wang
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Cluster analysis of microbiome data by using mixtures of Dirichlet–multinomial regression models
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020The human gut microbiome is one of the fundamental components of our physiology, and exploring the relationship between biological and environmental covariates and the resulting taxonomic composition of a given microbial community is an active area of ...
Sanjeena Subedi +3 more
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Aquaculture Economics & Management, 2021
This study examined the impact of climate change adaptation strategies on the welfare status of aquaculture fish farmers in Southwest, Nigeria. Multistage sampling procedure was used in the selection of 288 respondents.
L. Oparinde
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This study examined the impact of climate change adaptation strategies on the welfare status of aquaculture fish farmers in Southwest, Nigeria. Multistage sampling procedure was used in the selection of 288 respondents.
L. Oparinde
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Multinomial Logistic Regression
Nursing Research, 2002When the dependent variable consists of several categories that are not ordinal (i.e., they have no natural ordering), the ordinary least square estimator cannot be used. Instead, a maximum likelihood estimator like multinomial logit or probit should be used.The purpose of this article is to understand the multinomial logit model (MLM) that uses ...
Chanyeong, Kwak, Alan, Clayton-Matthews
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Multinomial Logistic Regression Ensembles
Journal of Biopharmaceutical Statistics, 2013This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable ...
Kyewon, Lee +4 more
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Handbook of Regression Analysis With Applications in R, 2020
The generalized linear regression adapt linear regression with a transformation (link) and distribution (alternative to gaussian) with maxmimum-likelihood estimation.
Shane T. Mueller
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The generalized linear regression adapt linear regression with a transformation (link) and distribution (alternative to gaussian) with maxmimum-likelihood estimation.
Shane T. Mueller
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IEEE transactions on electromagnetic compatibility (Print), 2019
A lot of work has been done for the effects evaluation of electronic equipment due to high-power electromagnetic environments. The focus of the evaluation usually stays on whether the effects occur or not (“1” or “0”) during tests.
Kejie Li +3 more
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A lot of work has been done for the effects evaluation of electronic equipment due to high-power electromagnetic environments. The focus of the evaluation usually stays on whether the effects occur or not (“1” or “0”) during tests.
Kejie Li +3 more
semanticscholar +1 more source
Multinomial Least Angle Regression
IEEE Transactions on Neural Networks and Learning Systems, 2012Keerthi and Shevade (2007) proposed an efficient algorithm for constructing an approximate least angle regression least absolute shrinkage and selection operator solution path for logistic regression as a function of the regularization parameter. In this brief, their approach is extended to multinomial regression.
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Dirichlet-Multinomial Regression [PDF]
, 2005 In this paper we provide a Random-Utility based derivation of the Dirichlet-Multinomial regression and posit it as a convenient alternative for dealing with overdispersed multinomial data. We show that this model is a natural extension of McFadden's conditional logit for grouped data and show how it relates with count models. Finally, we use a data set
Paulo Guimaraes, Richard Lindrooth
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A mixed‐effects multinomial logistic regression model
Statistics in Medicine, 2003AbstractA mixed‐effects multinomial logistic regression model is described for analysis of clustered or longitudinal nominal or ordinal response data. The model is parameterized to allow flexibility in the choice of contrasts used to represent comparisons across the response categories.
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