Amultilevelmodel for ordinal data in generalized linearmixedmodels (GLMM) framework is developed to account for the inherent dependencies among observationswithin clusters. Motivated by a data set from the British Social Attitudes Panel Survey (BSAPS), the random district effects and respondent effects are incorporated into the linear predictor to accommodate the nested clusterings. The fixed (...

Four heuristic algorithms are developed for solving the generalized version of tardiness flow shop problems. We consider the generalized tardiness flow shop model with minimization of the total tardiness as its performance measure. We modify the concept of cost over time (COVERT) for the generalized version of the flow shop tardiness model and employ this concept for developing four algorithms....

Companies in the same industry sector are usually stronger correlated than firms in different sectors, as they are similarly affected by macroeconomic effects, political decisions, and consumer trends. In spite of many stock return models taking account of this fact there are only a few credit default models taking it into consideration. In this paper we present a default model based on nested ...

Inferring information related to users enables to highly improve the quality of many mobile services. For example, knowing the demographic characteristics of a user allows a service to display more accurate information. According to the literature, various works present models to detect them but, to the best of our knowledge, no one is based on the use of the spatio-temporal entropy and introdu...

In the paper we propose nonparametric approaches for elearning data. In particular we want to supply a measure of the relative exercises importance, to estimate the acquired Knowledge for each student and finally to personalize the e-learning platform. The methodology employed is based on a comparison between nonparametric statistics for kernel density classification and parametric models such ...