Assessment of Effects of Age and Gender on the Incubation Period of COVID-19 with a Mixture Regression Model

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

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Finite mixture regression model with random effects: application to neonatal hospital length of stay

Computational Statistics & Data Analysis ◽

10.1016/s0167-9473(02)00180-9 ◽

2003 ◽

Vol 41 (3-4) ◽

pp. 359-366 ◽

Cited By ~ 31

Author(s):

Kelvin K.W. Yau ◽

Andy H. Lee ◽

Angus S.K. Ng

Keyword(s):

Length Of Stay ◽

Regression Model ◽

Random Effects ◽

Hospital Length ◽

Finite Mixture ◽

Hospital Length Of Stay ◽

Mixture Regression ◽

Finite Mixture Regression ◽

Mixture Regression Model

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Missing Posterior Teeth and Risk of Temporomandibular Disorders

Journal of Dental Research ◽

10.1177/0022034509344387 ◽

2009 ◽

Vol 88 (10) ◽

pp. 942-945 ◽

Cited By ~ 41

Author(s):

M.Q. Wang ◽

F. Xue ◽

J.J. He ◽

J.H. Chen ◽

C.S. Chen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Young Women ◽

Temporomandibular Disorders ◽

Logistic Model ◽

Logistic Regression Model ◽

Age And Gender ◽

Posterior Teeth ◽

Men 1 ◽

And Gender

There is disagreement about the association between missing posterior teeth and the presence of temporomandibular disorders (TMD). Here, the purpose was to investigate whether the number of missing posterior teeth, their distribution, age, and gender are associated with TMD. Seven hundred and forty-one individuals, aged 21–60 years, with missing posterior teeth, 386 with and 355 without TMD, were included. Four variables—gender, age, the number of missing posterior teeth, and the number of dental quadrants with missing posterior teeth—were analyzed with a logistic regression model. All four variables—gender (OR = 1.59, men = 1, women = 2), age (OR = 0.98), the number of missing posterior teeth (OR = 0.51), and the number of dental quadrants with missing posterior teeth (OR = 7.71)—were entered into the logistic model (P < 0.01). The results indicate that individuals who lose posterior teeth, with fewer missing posterior teeth but in more quadrants, have a higher prevalence of TMD, especially young women.

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