scholarly journals Comparison Between Two Multinomial Overdispersion Models Through Simulation

2020 ◽  
Vol 68 (1) ◽  
pp. 45-48
Author(s):  
Farzana Afroz ◽  
Zillur Rahman Shabuz
Keyword(s):  
Mixture Model ◽  
Simulation Study ◽  
Multinomial Distribution ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Multinomial Model ◽  
Pollen Data ◽  
Multinomial Data ◽  
The Difference ◽  
Overdispersion Parameter

A key assumption when using the multinomial distribution is that the observations are independent. In many practical situations, the observations could be correlated or clustered and the probabilities within each cluster might vary, which may lead to overdispersion. In this paper we discuss two well-known approaches to model overdispersed multinomial data, the Dirichlet-multinomial model and the finite-mixture model. The difference between these two models has been illustrated via simulation study. The forest pollen data is considered as a practical example of overdisperse multinomial data. The overdispersion parameter,φ, has been estimated using two classical estimators. Dhaka Univ. J. Sci. 68(1): 45-48, 2020 (January)

scholarly journals Studying the Third Cumulant of the Mixture of Dirichlet-multinomial Distributions

2021 ◽  
Vol 69 (2) ◽  
pp. 96-100
Author(s):  
Farzana Afroz
Keyword(s):  
Mean Squared Error ◽  
Multinomial Distribution ◽  
Finite Mixture ◽  
Multinomial Data ◽  
The Third ◽  
Squared Error ◽  
Lack Of Fit ◽  
Mathematical Expressions ◽  
Overdispersion Parameter ◽  
Deviance Statistic

Traditionally, the overdispersion parameter ϕ is estimated by using Pearson’s lack of fit statistic X2or the Deviance statistic D, which do not perform well in the case of sparse data. This paper particularly focuses on an estimator ϕnew of overdispersion parameter which was proposed for sparse multinomial data. The estimator was derived on the basis of an assumption on the 3rd cumulant of the response variable.When the data comes from the Dirichlet-multinomial distribution ϕnew is known to have the lowest root mean squared error comparing to the other three estimators. In this paper the 1st to 3rd order raw moments of the finite mixture of Dirichlet-multinomial distributions are derived, which results in complicated mathematical expressions. Furthermore, it is found that the 3rd cumulant of this mixture does not satisfy the assumption which is considered in the derivation of ϕnew . Dhaka Univ. J. Sci. 69(2): 96-100, 2021 (July)

scholarly journals Adaptive Weighted Finite Mixture Model

2020 ◽  
Vol 1 (3) ◽  
pp. 1-16
Author(s):  
Xin Xu ◽  
Yanjie Fu ◽  
Jingyi Wu ◽  
Yuqi Wang ◽  
Zeyu Huang ◽  
...  
Keyword(s):  
Mixture Model ◽  
Finite Mixture Model ◽  
Finite Mixture

Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model

2012 ◽  
Vol 49 (3) ◽  
pp. 313-335 ◽  
Author(s):  
Fabio Attorre ◽  
Fabio Francesconi ◽  
Michele De Sanctis ◽  
Marco Alfò ◽  
Francesca Martella ◽  
...  
Keyword(s):  
Mixture Model ◽  
Potential Distribution ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Forest Types

13C-urea breath test forHelicobacter pylori in young children: cut-off point determination by finite mixture model

2004 ◽  
Vol 23 (13) ◽  
pp. 2049-2060 ◽  
Author(s):  
Joanna X. Du ◽  
Terry Watkins ◽  
Luis E. Bravo ◽  
Elizabeth T. H. Fontham ◽  
M. Constanza Camargo ◽  
...  
Keyword(s):  
Young Children ◽  
Mixture Model ◽  
Breath Test ◽  
Finite Mixture Model ◽  
Urea Breath Test ◽  
Finite Mixture ◽  
Point Determination ◽  
13C Urea Breath Test
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