The knowledge of the climate pattern for a particular region is important taking appropriate actions to alleviate impact change. It also equally water resource planning and management purposes. In this study, regional disparities similarities have been revealed among different stations in Bangladesh based on an adaptive clustering algorithms that include hierarchical clustering, partitioning ar...

Longitudinal data clustering is challenging because the grouping has to account for similarity of individual trajectories in presence sparse and irregular times observation. This paper puts forward a hierarchical agglomerative method based on dissimilarity metric that quantifies cost merging two distinct groups curves, which are depicted by B-splines repeatedly measured data. Extensive simulati...

When data come from an unobserved heterogeneous population, common factor analysis is not appropriate to estimate the underlying constructs of interest. By replacing the traditional assumption of Gaussian distributed factors by a finite mixture of multivariate Gaussians, the unobserved heterogeneity can be modelled by latent classes. In so doing we obtain a particular factor mixture analysis wi...

This paper proposes a new clustered correlation multivariate GARCH model (CCMGARCH) that allows conditional correlations to form clusters. This model can generalize the time-varying correlation structure in Tse and Tsui (2002) by determining a natural grouping of the correlations among the series. To estimate the proposed model, we adopt Markov Chain Monte Carlo methods. Two efficient sampling ...

Probabilistic model-based clustering, based on nite mixtures of multivariate models, is a useful framework for clustering data in a statistical context. This general framework can be directly extended to clustering of sequential data, based on nite mixtures of sequential models. In this paper we consider the problem of tting mixture models where both multivariate and sequential observations are...

The problem of complex data analysis is a central topic of modern statistical science and learning systems and is becoming of broader interest with the increasing prevalence of highdimensional data. The challenge is to develop statistical models and autonomous algorithms that are able to acquire knowledge from raw data for exploratory analysis, which can be achieved through clustering technique...