Asymptotic normality of an adaptive kernel density estimator for finite mixture models

2006 ◽  
Vol 76 (2) ◽  
pp. 211-220 ◽  
Author(s):  
R.J. Karunamuni ◽  
T.N. Sriram ◽  
J. Wu
Keyword(s):  
Asymptotic Normality ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Finite Mixture ◽  
Density Estimator ◽  
Adaptive Kernel

Nonparametric Probabilistic Unbalanced Power Flow With Adaptive Kernel Density Estimator

2019 ◽  
Vol 10 (3) ◽  
pp. 3292-3300 ◽  
Author(s):  
Hossein Nosratabadi ◽  
Mohammad Mohammadi ◽  
Amin Kargarian
Keyword(s):  
Power Flow ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Adaptive Kernel

TH-C-T-617-01: A Proposed Alternative to Phase-Space Recycling Using Adaptive Kernel Density Estimator

2005 ◽  
Vol 32 (6Part21) ◽  
pp. 2164-2164
Author(s):  
N Tyagi ◽  
W Martin ◽  
J Du ◽  
A Bielajew ◽  
I Chetty
Keyword(s):  
Phase Space ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Adaptive Kernel

Fast adaptive kernel density estimator for data streams

2013 ◽  
Vol 42 (2) ◽  
pp. 285-317 ◽  
Author(s):  
Arnold P. Boedihardjo ◽  
Chang-Tien Lu ◽  
Feng Chen
Keyword(s):  
Data Streams ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Adaptive Kernel

scholarly journals A proposed alternative to phase-space recycling using the adaptive kernel density estimator method

2006 ◽  
Vol 33 (2) ◽  
pp. 553-560 ◽  
Author(s):  
Neelam Tyagi ◽  
William R. Martin ◽  
J. Du ◽  
A. F. Bielajew ◽  
Indrin J. Chetty
Keyword(s):  
Phase Space ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Density Estimator ◽  
Adaptive Kernel

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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