Estimation of Parameters of a Finite Mixture of Distributions

1968 ◽  
Vol 30 (3) ◽  
pp. 472-482
Author(s):  
A. B. M. Lutful Kabir
Keyword(s):  
Finite Mixture ◽  
Mixture Of Distributions ◽  
Estimation Of Parameters ◽  
Finite Mixture Of Distributions

scholarly journals Revisiting Structural Breaks in the Terms of Trade of Primary Commodities (1900–2020)—Markov Switching Models and Finite Mixture Distributions

2021 ◽  
Vol 5 (1) ◽  
pp. 34
Author(s):  
Armand Taranco ◽  
Vincent Geronimi
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Structural Breaks ◽  
Terms Of Trade ◽  
Finite Mixture ◽  
Mixture Of Distributions ◽  
Data Set ◽  
Markov Switching Models ◽  
Finite Mixture Of Distributions

This paper presents an analysis of the long-term dynamics of the terms of trade of primary commodities (TTPC) using an extended data set for the whole period 1900–2020. Following our original contribution, we implement three approaches of time series—the finite mixture of distributions, the Markov finite mixture of distributions, and the Markov regime-switching model. Our results confirm the hypothesis of the existence of a succession of three different dynamic regimes in the TTPC over the 1900–2020 period. It seems that the uncertainty characterising the long-term dynamic analysis of TTPC is better taken into account with a Markov hypothesis in the transition from one regime to another than without this hypothesis. In addition, this hypothesis improves the quality of the time series segmentation into regimes.

Merging the components of a finite mixture using posterior probabilities

2017 ◽  
Vol 19 (2) ◽  
pp. 109-139
Author(s):  
Marc Comas-Cufí ◽  
Josep A Martín-Fernández ◽  
Glòria Mateu-Figueras
Keyword(s):  
Finite Mixture ◽  
Mixture Component ◽  
Posterior Probabilities ◽  
Mixture Of Distributions ◽  
New Techniques ◽  
Number Of Clusters ◽  
New Methods ◽  
Cluster A ◽  
Log Ratio ◽  
Finite Mixture Of Distributions

Methods in parametric cluster analysis commonly assume data can be modelled by means of a finite mixture of distributions. However, associating each mixture component to one cluster is frequently misleading because different mixture components can overlap, and then, associated clusters can overlap too suggesting a unique cluster. A number of approaches have already been proposed to construct the clusters by merging components using the posterior probabilities. This article presents a generic approach for building a hierarchy of mixture components that integrates and generalizes some techniques proposed earlier in the literature. Using this proposal, two new techniques based on the log-ratio of posterior probabilities are introduced. Moreover, to decide the final number of clusters, two new methods are presented. Simulated and real datasets are used to illustrate this methodology.

Sign in / Sign up

Export Citation Format

Share Document