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Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression
Cluster-weighted modeling finite mixtures of regression EM-algorithm
2012/9/19
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
About the posterior distribution in hidden Markov Models with unknown number of states
Hidden Markov models number of components order selection Bayesian statistics posterior distribution
2012/9/19
In this paper, we investigate the asymptotic behaviour of the posterior distribution in hidden Markov models (HMMs) when using Bayesian methodology. We obtain a general asymptotic result, and give con...
Grouping Strategies and Thresholding for High Dimensional Linear Models
Structured sparsity Grouping, Learning Theory Non Linear Methods Block-thresholding coherence Wavelets
2012/9/19
The estimation problem in a high regression model with structured sparsity is investigated.An algorithm using a two steps block thresholding procedure called GR-LOL is provided.Convergence rates are p...
Estimating a Causal Order among Groups of Variables in Linear Models
Causal Order among Groups Variables in Linear Models
2012/9/19
The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among...
Generalized Interference Models in Doubly Stochastic Poisson Random Fields for Wideband Communications: the PNSC(alpha) model
Interference models Cox Process Doubly Stochastic Poisson Stable Process Isotropicα-stable Complexα-stable
2012/9/19
A general stochastic model is developed for the total interference in wideband systems, denoted as the PNSC(α) Interference Model. It allows one to obtain, analytic representations in situations where...
Optimal inferential models for a Poisson mean
Belief function constraint plausibility function predic-tive random set recursive ordering score function validity.
2012/9/18
Statistical inference on the mean of a Poisson distribution is a fundamentally important problem with modern applications in, e.g., particle physics. The dis-creteness of the Poisson distribution make...
PC algorithm for Gaussian copula graphical models
Copula covariance matrix graphical model model selection multi-variate normal distribution nonparanormal distribution.
2012/9/18
The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs. In Gaussian mod-els, tests of conditional independence are typically based ...
Entrepreneurial education’s and entrepreneurial role models’influence on career choice
Entrepreneurial education’s entrepreneurial role models’influence career choice
2012/10/17
Little research has been done into the impact of entrepreneurial education and entrepreneurial role models on entrepreneurship as a career choice, especially in developing countries.
On non-stationary threshold autoregressive models
explosive TAR(1) model least-squares estimator unit root TAR(1) model
2011/7/19
In this paper we study the limiting distributions of the least-squares estimators for the non-stationary first-order threshold autoregressive (TAR(1)) model. It is proved that the limiting behaviors o...
Linear Latent Force Models using Gaussian Processes
Gaussian Processes Linear Latent Force Models
2011/7/19
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.
Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Diverging number of parameters Feature selection
2011/7/19
In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problem...
Statistical Topic Models for Multi-Label Document Classification
Topic Models LDA Multi-Label Classification Document Modeling
2011/7/19
Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches i...
Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches i...
Sequential Monte Carlo EM for multivariate probit models
Maximum likelihood Multivariate probit Monte Carlo EM adaptive sequential Monte Carlo
2011/7/19
A Monte Carlo EM algorithm is considered for the maximum likelihood estimation of multivariate probit models.
Modelling outliers and structural breaks in dynamic linear models with a novel use of a heavy tailed prior for the variances: An alternative to the Inverted Gamma
Modelling outliers structural breaks Inverted Gamma
2011/7/19
In this paper we propose a new wider class of hypergeometric heavy tailed priors that are given as the convolution of a Student-t density for the location parameter and a Scaled Beta2 prior for the va...