Quadratic stochastic operators generated by mixture distributions

2021 ◽  
Author(s):  
Nasir Ganikhodjaev ◽  
Ftameh Khaled
Keyword(s):  
Mixture Distributions ◽  
Quadratic Stochastic Operators

Application of mixture distributions for identifying thresholds of frequent and high inpatient mental health service use in longitudinal data

2021 ◽  
pp. 1-10
Author(s):  
Bianca Suesse ◽  
Luise Lago ◽  
Victoria Westley-Wise ◽  
Malcolm Masso ◽  
Jose Cuenca ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Health Service ◽  
Longitudinal Data ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use ◽  
Mixture Distributions ◽  
Inpatient Mental Health

scholarly journals Projective surjectivity of quadratic stochastic operators on L1 and its application

2021 ◽  
Vol 148 ◽  
pp. 111034
Author(s):  
Farrukh Mukhamedov ◽  
O. Khakimov ◽  
A. Fadillah Embong
Keyword(s):  
Quadratic Stochastic Operators

Multivariate process control chart based on sign and signed-rank tests for mixture distributions

2021 ◽  
pp. 1-20
Author(s):  
Akaninyene Udo Udom ◽  
Offorma S. Ugwu ◽  
Everetus O. Ossai ◽  
Nse S. Udoh
Keyword(s):  
Process Control ◽  
Control Chart ◽  
Mixture Distributions ◽  
Rank Tests ◽  
Multivariate Process ◽  
Multivariate Process Control ◽  
Signed Rank

scholarly journals Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions

2018 ◽  
Vol 15 (03) ◽  
pp. 1850012 ◽  
Author(s):  
Andrzej Polanski ◽  
Michal Marczyk ◽  
Monika Pietrowska ◽  
Piotr Widlak ◽  
Joanna Polanska
Keyword(s):  
Dynamic Programming ◽  
Mixture Models ◽  
Recursive Algorithm ◽  
Dynamic Programming Method ◽  
Mixture Distributions ◽  
Gaussian Mixtures ◽  
Scoring Functions ◽  
Initial Values ◽  
The Em Algorithm

Setting initial values of parameters of mixture distributions estimated by using the EM recursive algorithm is very important to the overall quality of estimation. None of the existing methods are suitable for heteroscedastic mixtures with a large number of components. We present relevant novel methodology of estimating the initial values of parameters of univariate, heteroscedastic Gaussian mixtures, on the basis of dynamic programming partitioning of the range of observations into bins. We evaluate variants of the dynamic programming method corresponding to different scoring functions for partitioning. We demonstrate the superior efficiency of the proposed method compared to existing techniques for both simulated and real datasets.

Identifying spatial mixture distributions of PM2.5 and PM10 in Taiwan during and after a dust storm

2012 ◽  
Vol 54 ◽  
pp. 728-737 ◽  
Author(s):  
Hone-Jay Chu ◽  
Hwa-Lung Yu ◽  
Yi-Ming Kuo
Keyword(s):  
Dust Storm ◽  
Mixture Distributions ◽  
Pm2.5 And Pm10
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