This paper investigates the performance of three types of random coefficients logistic regression models; that is, models using parametric, semi-parametric, and nonparametric specifications of the distribution of the random effects. Whereas earlier studies focussed on models with a single random effect, here we look at models with multidimensional random effects (intercepts and slopes). Moreove...

Some quality characteristics are well defined when treated as response variables and are related to some independent variables. This relationship is called a profile. Parametric models, such as linear models, may be used to model profiles. However, in practical applications due to the complexity of many processes it is not usually possible to model a process using parametric models.In these cas...

The linear mixed model (LMM) is a popular statistical for the analysis of longitudinal data. However, robust estimation and inferential conclusions LMM in presence outliers (i.e., observations with very low probability occurrence under Normality) not part mainstream data analysis. In this work, we compared coverage rates confidence intervals (CIs) ba...

in recent years, the existing competitions between investment companies have been increased largely by entering private investors in capital market. large and powerful companies try to achieve the goals predicted to increase the competition capacity. to analyze the efficiency of investment companies, parametric and non-parametric methods are used. in this research, based on the dissociation pow...

In this work, we discuss the parametric modeling for the (electro)-thermal analysis of components of nanoelectronic structures and automatic model order reduction of the consequent parametric models. Given the system matrices at different values of the parameters, we introduce a simple method of extracting system matrices which are independent of the parameters, so that parametric models of a c...