Results 221 to 230 of about 160,280 (251)
Some of the next articles are maybe not open access.
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
openaire +2 more sources
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
openaire +2 more sources
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.
openaire +2 more sources
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
openaire
Interpreting multinomial logistic regression [PDF]
Stata Technical Bulletin, 1994 Social and biological scientists widely use logit (logistic) regression to model binary dependent variables such as move/stay or live/die. Techniques for modeling multiple-category dependent variables are a relatively recent development, however. Asking Stata to perform multinomial logistic regression is easy; given a Y with three or more unordered ...
Hamilton, Lawrence C. +1 more
openaire
Multinomial logistic regression algorithm
Annals of the Institute of Statistical Mathematics, 1992zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
Multinomial Logistic Regression Analysis
2017The usage of mixed methods approach on qualitative data has been exemplified in this chapter. The chapter presents the relevance of using multinomial regression approach in the study and discusses its results. The chapter is insightful for readers looking forward to learning practical applications of quantitative techniques on qualitative data.
Nausheen Nizami, Narayan Prasad
openaire +1 more source
Multinomial Squared Direction Cosines Regression
The 2011 International Joint Conference on Neural Networks, 2011In this paper we introduce Multinomial Squared Direction Cosines Regression as an alternative Multinomial Response Model. The proposed model offers an intuitive geometric interpretation to the task of estimating posterior class probabilities in multi-class problems.
Naveed H. Iqbal +1 more
openaire +1 more source
Multinomial Latent Logistic Regression for Image Understanding
IEEE Transactions on Image Processing, 2016In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of logistic regression, MLLR is efficiently optimized using the second-order derivatives and provides effective probabilistic analysis on output predictions.
Zhe, Xu +5 more
openaire +2 more sources