This paper extends the Bayes marginal model plot (BMMP) model assessment technique from a traditional logistic regression setting to a multilevel application in the area of criminal justice. Convicted felons in the United States receive either a prison sentence or a less severe jail or non-custodial sentence. Researchers have identified many determinants of sentencing variation across the count...

    Background: Aging is a major challenge not only for high-income countries but also for middle- and low-income countries. The length of stay (LOS) in hospitals is one of the major concerns of elderly patients, which should be taken into consideration. We aimed to investigate the factors affecting LOS of elderly patients admitted to a referral hospital of northeast of Iran.    Methods: A rel...

This paper describes the development of a model for identifying points of prominence in speech. This model can be used as a first step in intonational labeling of corpora that are used in some speech synthesis systems (Black and Taylor, 1995). The working definition of prominence is that starred ToBI accents (Silverman et al., 1992), that is, H*, L*, L*+H, L+H*, and H+!H*, are prominent. The pr...

A mixed-effects multinomial logistic regression model is described for analysis of clustered or longitudinal nominal or ordinal response data. The model is parameterized to allow flexibility in the choice of contrasts used to represent comparisons across the response categories. Estimation is achieved using a maximum marginal likelihood (MML) solution that uses quadrature to numerically integra...

Logistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situa...

A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right truncation count data was developed. In this model, we considered a response variab...

‎Zero-inflated negative binomial model is an appropriate choice to count response variables with excessive zeros and over-dispersion simultaneously. ‎This paper addressed parameter estimation in the zero-inflated when there are many parameters, ‎so that some of them have not influence on variable. ‎We proposed based linear shrinkage, ‎pretest, ‎shrinkage pretest, ‎Stein-type, ‎and positive Stei...