Use of Factor Analyzer Normal Mixture Model with Mean Pattern Modeling on Clustering Genes
نویسندگان
چکیده
منابع مشابه
Mixture of Normal Mean-Variance of Lindley Distributions
‎Abstract: In this paper, a new mixture modelling using the normal mean-variance mixture of Lindley (NMVL) distribution has been considered. The proposed model is heavy-tailed and multimodal and can be used in dealing with asymmetric data in various theoretic and applied problems. We present a feasible computationally analytical EM algorithm for computing the maximum likelihood estimates. T...
متن کاملmclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation
mclust is a contributed R package for model-based clustering, classification, and density estimation based on finite normal mixture modeling. It provides functions for parameter estimation via the EM algorithm for normal mixture models with a variety of covariance structures, and functions for simulation from these models. Also included are functions that combine model-based hierarchical cluste...
متن کاملBayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of compone...
متن کاملGrowth mixture modeling with non-normal distributions.
A limiting feature of previous work on growth mixture modeling is the assumption of normally distributed variables within each latent class. With strongly non-normal outcomes, this means that several latent classes are required to capture the observed variable distributions. Being able to relax the assumption of within-class normality has the advantage that a non-normal observed distribution do...
متن کاملOn choosing a mixture model for clustering
Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classification algorithm based on the classification likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AICrespectively BIC-type approximations are applied. The resulting criteria turn out to be the sum of the AIC or BIC rela...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2006
ISSN: 2287-7843
DOI: 10.5351/ckss.2006.13.1.113