منابع مشابه
Fitting finite mixtures of generalized linear regressions in R
R package flexmix provides flexible modelling of finite mixtures of regression models using the EM algorithm. Several new features of the software such as fixed and nested varying effects for mixtures of generalized linear models and multinomial regression for a-priori probabilities given concomitant variables are introduced. The use of the software in addition to model selection is demonstrate...
متن کاملLearning Mixtures of Linear Regressions with Nearly Optimal Complexity
Mixtures of Linear Regressions (MLR) is an important mixture model with many applications. In this model, each observation is generated from one of the several unknown linear regression components, where the identity of the generated component is also unknown. Previous works either assume strong assumptions on the data distribution or have high complexity. This paper proposes a fixed parameter ...
متن کاملSpectral Experts for Estimating Mixtures of Linear Regressions
Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima. In this paper, we develop a new computationally efficient and provably consistent estimator for a mixture of linear regressions, a simple instance of a discriminative latentvariable model. Our approach relies on a lowrank linear regression to recover a symmetric t...
متن کاملMixtures of Self-Modelling Regressions
A shape invariant model for functions f1,...,fn specifies that each individual function fi can be related to a common shape function g through the relation fi(x)=aig(cix + di) + bi. We consider a flexible mixture model that allows multiple shape functions g1,...,gK, where each fi is a shape invariant transformation of one of those gk. We derive an MCMC algorithm for fitting the model using Baye...
متن کاملMixtures of Self-Modeling Regressions
A shape invariant model for functions f1, . . . , fn specifies that each individual function fi can be related to a common shape function g through the relation fi(x) = aig(cix + di) + bi. We consider a mixture model that allows multiple shape functions g1, . . . , gK , where each fi is a shape invariant transformation of one of those gk. We derive an MCMC algorithm for fitting the model using ...
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: Journal of Statistical Computation and Simulation
سال: 2010
ISSN: 0094-9655,1563-5163
DOI: 10.1080/00949650802590261