EM-ALGORITHM

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A Normal Weighted Inverse Gaussian Distribution for Skewed and Heavy-Tailed Data
《Applied Mathematics》2022年第2期163-177,共15页Calvin B. Maina Patrick G. O. Weke Carolyne A. Ogutu Joseph A. M. Ottieno 
High frequency financial data is characterized by non-normality: asymmetric, leptokurtic and fat-tailed behaviour. The normal distribution is therefore inadequate in capturing these characteristics. To this end, vario...
关键词:Inverse Gaussian Finite Mixture Weighted Distribution Mixed Model EM-ALGORITHM 
Discrete-time inverse linear quadratic optimal control over fnite time-horizon under noisy output measurements被引量:1
《Control Theory and Technology》2021年第4期563-572,共10页Han Zhang Yibei Li Xiaoming Hu 
In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is considered.Our goal is to recover the corresponding quadratic objective function using noisy o...
关键词:Inverse optimal control Linear quadratic regulator Statistical consistency EM-ALGORITHM 
A Finite Mixture of Generalised Inverse Gaussian with Indexes -1/2 and -3/2 as Mixing Distribution for Normal Variance Mean Mixture with Application
《Open Journal of Statistics》2021年第6期963-976,共14页Calvin B. Maina Patrick G. O. Weke Carolyne A. Ogutu Joseph A. M. Ottieno 
Mixture models have become more popular in modelling compared to standard distributions. The mixing distributions play a role in capturing the variability of the random variable in the conditional distribution. Studie...
关键词:Finite Mixture Weighted Distribution Mixed Model EM-ALGORITHM 
A Special Weight for Inverse Gaussian Mixing Distribution in Normal Variance Mean Mixture with Application
《Open Journal of Statistics》2021年第6期977-992,共16页Calvin B. Maina Patrick G. O. Weke Carolyne A. Ogutu Joseph A. M. Ottieno 

Normal Variance-Mean Mixture (NVMM) provides

关键词:Finite Mixture Weighted Distribution Mixed Model EM-ALGORITHM 
Maximum Likelihood Estimation of the Parameters of Exponentiated Generalized Weibull Based on Progressive Type II Censored Data被引量:4
《Open Journal of Statistics》2017年第6期956-963,共8页Ibrahim Sawadogo Leo Odongo Ibrahim Ly 
Exponentiated Generalized Weibull distribution is a probability distribution which generalizes the Weibull distribution introducing two more shapes parameters to best adjust the non-monotonic shape. The parameters of ...
关键词:MAXIMUM LIKELIHOOD Type II Censored Data Exponentiated GENERALIZED Weibull EM-ALGORITHM 
Statistical Inference for the Parameter of Pareto Distribution Based on Progressively Type-I Interval Censored Sample
《Communications in Mathematical Research》2014年第4期345-357,共13页Abdalroof M.S. Zhao Zhi-wen Wang De-hui 
The NSF (11271155,11001105,11071126,10926156 and 11071269) of China;the Specialized Research Fund (20110061110003 and 20090061120037) for the Doctoral Program of Higher Education;the General Humanities and Social Science Research Projects (11YJAZH125) Sponsored by Ministry of Education;the Science and Technology Development Program (201201082) of Jilin Province
In this paper, the estimation of parameters based on a progressively typeI interval censored sample from a Pareto distribution is studied. Different methods of estimation are discussed, which include mid-point approxi...
关键词:EM-ALGORITHM maximum likelihood estimation method of moment Bayes estimation 
Mixture Normal Models in which the Proportions of Susceptibility are Related to Dose Levels
《Acta Mathematicae Applicatae Sinica》2010年第3期463-472,共10页Bing He Min Chen Li-xin Song De-hui Wang 
Supported by the National Natural Science Foundation of China (No. 10571073);Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070183023);Program for New Century Excellent Talents in University, Scientific Research Fund of Jilin University (No. 200810024)
A problem that frequently occurs in biological experiments with laboratory animals is that some subjects are less susceptible to the treatment group than others. Finite mixture models have traditionally been used to d...
关键词:DOSE-RESPONSE EM-ALGORITHM mixture normal models SUSCEPTIBILITY 
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