Fitting finite mixtures of generalized linear regressions in R

@article{Grn2007FittingFM,
  title={Fitting finite mixtures of generalized linear regressions in R},
  author={Bettina Gr{\"u}n and Friedrich Leisch},
  journal={Comput. Stat. Data Anal.},
  year={2007},
  volume={51},
  pages={5247-5252}
}
  • B. Grün, F. Leisch
  • Published 1 July 2007
  • Computer Science, Mathematics
  • Comput. Stat. Data Anal.
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References

SHOWING 1-10 OF 15 REFERENCES
Fitting Finite Mixtures of Linear Regression Models with Varying & Fixed Eects in R
A general model class of finite mixtures of linear regression models is presented. It allows (nested) varying and fixed effects for the regression coefficients and the variance. A combination of…
FlexMix: A general framework for finite mixture models and latent class regression in R
TLDR
FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment and provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models.
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
TLDR
An assessing method of mixture model in a cluster analysis setting with integrated completed likelihood appears to be more robust to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.
Identifiablity of Models for Clusterwise Linear Regression
The model choice and the interpretation of the parameters are discussed as well as the use of the identifiability concept for fixed partition models. The concept is generalized to "partial…
Meta-analysis by random effect modelling in generalized linear models.
  • M. Aitkin
  • Mathematics
    Statistics in medicine
  • 1999
TLDR
This paper argues for the general use of random effect models, and illustrates the value of non-parametric maximum likelihood (NPML) analysis of multi-centre trials.
Concomitant-Variable Latent-Class Models
Abstract This article introduces and illustrates a new type of latent-class model in which the probability of latent-class membership is functionally related to concomitant variables with known…
Generalizing Logistic Regression by Nonparametric Mixing
Abstract Logistic regression is a common technique for analyzing the effect of a covariate vector x on the number of successes y in m trials when y has a binomial distribution. But at times either…
Generalized Linear Models
TLDR
This is the Ž rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
Lexical Scope and Statistical Computing
TLDR
The concept of scoping rules is discussed and how lexical scope can enhance the functionality of a language is shown.
Generalized Linear Models
  • 1989
...
...