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Showing 3 results for Em Algorithm
Mohammad Bahrami, ,
Volume 22, Issue 2 (3-2018)
Abstract
Abstract One of the main goal in the mixture distributions is to determine the number of components. There are different methods for determination the number of components, for example, Greedy-EM algorithm which is based on adding a new component to the model until satisfied the best number of components. The second method is based on maximum entropy and finally the third method is based on nonparametric. In this manuscript it is considered the mixture distributions with Skew-t-Normal components.
Ali Reza Taheriyoun, Gazelle Azadi,
Volume 26, Issue 1 (12-2021)
Abstract
Profile monitoring is usually faced by control charts and mostly the response variable is observable in those problems. We confront here with a similar problem where the values of the reward function are observed instead of the response variable vector and we use the dart model to make it easier to understand. Supposing there exists at most one change-point, a sequence of independent points resulted by darts throws is observed and the estimation of parameters and the change-point (if there exists any) are presented using the frequentist and Bayesian approaches. In both the approaches, two possible precision scalar and matrix are studied separately. The results are examined through a simulation study and the methods applied on a real data.
Taban Baghfalaki, Parvaneh Mehdizadeh, Mahdy Esmailian,
Volume 26, Issue 1 (12-2021)
Abstract
Joint models use in follow-up studies to investigate the relationship between longitudinal markers and survival outcomes
and have been generalized to multiple markers or competing risks data. Many statistical achievements in the field of joint
modeling focuse on shared random effects models which include characteristics of longitudinal markers as explanatory variables
in the survival model. A less-known approach is the joint latent class model, assuming that a latent class structure
fully captures the relationship between the longitudinal marker and the event risk. The latent class model may be appropriate
because of the flexibility in modeling the relationship between the longitudinal marker and the time of event, as well as the
ability to include explanatory variables, especially for predictive problems. In this paper, we provide an overview of the joint
latent class model and its generalizations. In this regard, first a review of the discussed models is introduced and then the
estimation of the model parameters is discussed. In the application section, two real data sets are analyzed.
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