Estimators for the parameters of a finite mixture of distributions

1969 ◽  
Vol 21 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Keewhan Choi
Keyword(s):  
Finite Mixture ◽  
Mixture Of Distributions ◽  
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.

Extended Weibull type distribution and finite mixture of distributions

2006 ◽  
Vol 3 (3) ◽  
pp. 224-233 ◽  
Author(s):  
Jamal A. Al-Saleh ◽  
Satish K. Agarwal
Keyword(s):  
Finite Mixture ◽  
Mixture Of Distributions ◽  
Type Distribution ◽  
Finite Mixture Of Distributions

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.

Controlling in der Produktentwicklung

Controlling ◽  
2020 ◽  
Vol 32 (3) ◽  
pp. 45-50
Author(s):  
Marc Janka
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Finite Mixture

Gemeinhin gilt die Annahme, dass das Controlling für viele deutsche Unternehmen auch oder besonders in der Produktentwicklung von großer Bedeutung ist und vor allem unter Umfeldunsicherheit ein wesentlicher Erfolgsfaktor sein kann. Der vorliegende Beitrag zeigt unter Anwendung einer für die Controlling-Forschung neuartigen Methode zur Schätzung von Mischverteilungen mittels partieller Regressionen (englisch finite mixture partial least squares [FIMIX-PLS]), ob diese Annahme für alle Unternehmen gleichermaßen gilt.

Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis

2020 ◽  
Vol 14 (12) ◽  
pp. 1524-1533 ◽  
Author(s):  
Xinzhi Zhong ◽  
Yajie Zou ◽  
Zhi Dong ◽  
Shaoxin Yuan ◽  
Muhammad Ijaz
Keyword(s):  
Travel Time ◽  
Survival Model ◽  
Finite Mixture ◽  
Passenger Cars ◽  
Urban Arterial

scholarly journals A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 115
Author(s):  
Despoina Makariou ◽  
Pauline Barrieu ◽  
George Tzougas
Keyword(s):  
Decision Making ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Risk Measures ◽  
Finite Mixture ◽  
Parametric Family ◽  
Collective Decision Making ◽  
Multiple Sources ◽  
Mixture Modelling ◽  
Expert Opinions

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.

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