Add to ORKGWe develop a suitable reweighting approach to deal with outliers when maximum likelihood estimation is used to estimate latent class models. In such a context, the EM algorithm is used and the presence of outliers and spurious observations is common. The Proposed method is motivated by an application aimed at finding clusters of offending behaviours
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis wh...
Recently, several bias-adjusted stepwise approaches to latent class modeling with continuous distal ...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
The Expectation-Maximization (EM) algorithm is routinely used for maximum likelihood estimation in l...
The Expectation-Maximization (EM) algorithm is routinely used for maximum likelihood estimation in l...
The Expectation-Maximization (EM) algorithm is routinely used for the maximum likelihood estimation ...
The Expectation-Maximization (EM) algorithm is routinely used for maximum likelihood estimation in l...
The present paper is to give a new procedure of parameter estimation in a latent class model by mean...
International audienceThis chapter deals with mixture models for clustering categorical and mixed-ty...
Latent class analysis has been used in a wide variety of research contexts. One of the attractive fe...
Statistical models with latent structure have a history going back to the 1950s and have seen widesp...
The technique of latent class analysis relies on a number of model assumptions which might be violat...
59 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Marginal maximum likelihood es...
In the present work, a weighted maximum likelihood method (WMLM) is proposed to obtain robust estima...
We propose a model-based clustering procedure where each component can take into account cluster-spe...
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis wh...
Recently, several bias-adjusted stepwise approaches to latent class modeling with continuous distal ...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
The Expectation-Maximization (EM) algorithm is routinely used for maximum likelihood estimation in l...
The Expectation-Maximization (EM) algorithm is routinely used for maximum likelihood estimation in l...
The Expectation-Maximization (EM) algorithm is routinely used for the maximum likelihood estimation ...
The Expectation-Maximization (EM) algorithm is routinely used for maximum likelihood estimation in l...
The present paper is to give a new procedure of parameter estimation in a latent class model by mean...
International audienceThis chapter deals with mixture models for clustering categorical and mixed-ty...
Latent class analysis has been used in a wide variety of research contexts. One of the attractive fe...
Statistical models with latent structure have a history going back to the 1950s and have seen widesp...
The technique of latent class analysis relies on a number of model assumptions which might be violat...
59 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Marginal maximum likelihood es...
In the present work, a weighted maximum likelihood method (WMLM) is proposed to obtain robust estima...
We propose a model-based clustering procedure where each component can take into account cluster-spe...
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis wh...
Recently, several bias-adjusted stepwise approaches to latent class modeling with continuous distal ...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...