Search Results - subject:"Model based clustering"

Statistical computation and inference for functional data analysis

Jiang, Huijing - 2010

. Over all simulation settings, the spatial-functional clustering method outperforms existing model-based clustering methods … (level-2 clustering). Our primary focus is to evaluate a model-based clustering to more straightforward hard clustering …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10009475878

Clustering of high-dimensional data via finite mixture models

McLachlan, Geoff J.; Baek, Jangsun - 2010

Finite mixture models are being commonly used in a wide range of applications in practice concerning density estimation and clustering. An attractive feature of this approach to clustering is that it provides a sound statistical framework in which to assess the important question of how many...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10009448074

Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph - 2009

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011310688

A Bayesian approach to two-mode clustering

Paap, Richard; van Dijk, van Dijk, A.; van Rosmalen, … - Faculteit der Economische Wetenschappen, Erasmus … - 2009

We develop a new Bayesian approach to estimate the parameters of a latent-class model for the joint clustering of both modes of two-mode data matrices. Posterior results are obtained using a Gibbs sampler with data augmentation. Our Bayesian approach has three advantages over existing methods....

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010731570

Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph - Austrian Center for Labor Economics and the Analysis of … - 2009

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005044266

A Bayesian approach to two-mode clustering

Dijk, A. van; Rosmalen, J.M. van; Paap, R. - Erasmus University Rotterdam, Econometric Institute - 2009

. Keywords: two-mode data, model-based clustering, latent-class model, MCMC ⁄We thank Dennis Fok for helpful comments … model-based techniques for clustering data. For one-mode data, model-based clustering methods have been developed, see, for … example, Fraley and Raftery (1998); Wedel and Kamakura (2000); Fr˜uhwirth-Schnatter (2006). These model-based clustering …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005696120

Bayesian clustering of categorical time series using finite mixtures of Markov chain models

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph - 2009

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011343931

Mixtures of skew-t factor analyzers

Murray, Paula M.; Browne, Ryan P.; McNicholas, Paul D. - In: Computational Statistics & Data Analysis 77 (2014) C, pp. 326-335

Gaussian and t-distribution analogues, mixtures of skew-t factor analyzers are very well-suited for model-based clustering of …

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010871318

MultiLCIRT: An R package for multidimensional latent class item response models

Bartolucci, Francesco; Bacci, Silvia; Gnaldi, Michela - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 971-985

A class of Item Response Theory (IRT) models for binary and ordinal polytomous items is illustrated and an R package for dealing with these models, named MultiLCIRT, is described. The models at issue extend traditional IRT models allowing for multidimensionality and discreteness of latent...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010871332

Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition

Lin, Tsung-I - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 183-195

A framework of using t mixture models with fourteen eigen-decomposed covariance structures for the unsupervised learning of heterogeneous multivariate data with possible missing values is designed and implemented. Computationally flexible EM-type algorithms are developed for parameter estimation...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010871421