scholarly journals Study of the New Finite Mixture of Weibull Extension Model: Identifiability, Properties and Estimation

2022 ◽  
Vol 10 (1) ◽  
pp. 35-60
Author(s):  
Noura S. Mohamed ◽  
Moshira A. Ismail ◽  
Sanaa A. Ismail
Keyword(s):  
Finite Mixture ◽  
Model Identifiability ◽  
Extension Model

Hub-extension model and access to pediatric behavioral integrated primary care.

2020 ◽  
Vol 8 (3) ◽  
pp. 220-227
Author(s):  
Rachel G. Lilly ◽  
Tawnya J. Meadows ◽  
Jessica R. Sevecke-Hanrahan ◽  
Carrie E. Massura ◽  
Maria E. Golden ◽  
...  
Keyword(s):  
Primary Care ◽  
Integrated Primary Care ◽  
Extension Model

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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