Erratum to: Parameterizing mixture models with generalized moments

Generalized mixture models for molecular phylogenetic estimation.

The rapidly growing availability of multigene sequence data during the past decade has enabled phylogeny estimation at phylogenomic scales. However, dealing with evolutionary process heterogeneity across the genome becomes increasingly challenging. Here we develop a mixture model approach that uses reversible jump Markov chain Monte Carlo (MCMC) estimation to permit as many distinct models as t...

Generalized Mixture Models for Blind Source Separation

Neural Independent Component Analysis (ICA) algorithms based on unimodal source distributions provide acceptable performances in the case of Blind Source Separation (BSS) of super-gaussian sources. However, their convergence profiles are significantly slower in the case of sub-gaussian sources. In some situations it is necessary to deal with sub-gaussian signals in the form of noise or others. ...

The Negative Binomial Distribution Efficiency in Finite Mixture of Semi-parametric Generalized Linear Models

Introduction Selection the appropriate statistical model for the response variable is one of the most important problem in the finite mixture of generalized linear models. One of the distributions which it has a problem in a finite mixture of semi-parametric generalized statistical models, is the Poisson distribution. In this paper, to overcome over dispersion and computational burden, finite ...

The Welfare Cost of Inflation: Theory with an Application of Generalized Method of Moments (GMM) to Ira

Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario.

Parameter estimation for infectious disease models is important for basic understanding (e.g. to identify major transmission pathways), for forecasting emerging epidemics, and for designing control measures. Differential equation models are often used, but statistical inference for differential equations suffers from numerical challenges and poor agreement between observational data and determi...