A new family of distributions called type II exponentiated half logistic is introduced and studied. Four new special models are presented. Some mathematical properties of the new family are studied. Explicit expressions for the moments, probability weighted moments, quantile function, mean deviation, order statistics and Renyi entropy are investigated. Parameter estimation of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family.

This paper aims to detect which are the drivers leading to victory for basketball matches in NBA, the American National Basketball Association. First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.

It is common to assume a normal distribution when discriminating and classifying a multivariate data based on some attributes. But when such data is lighter or heavier in both tails than the normal distribution, then the probability of misclassification becomes higher giving unreliable result. This study proposed multivariate exponential power distribution a family of elliptically contoured model as underlining model for discrimination and classification. The distribution has a shape parameter which regulate the tail of the symmetric distribution to mitigate the problem of both lighter and heavier tails data, this generalizes the normal distribution and thus will definitely gives a lower misclassification error in discrimination and classification. The resulting discriminant model was compared with fisher linear discriminant function when applying to real data.

In consumer research, marketing, public policy and other fields, individ- uals’ choice depends on the number of possible alternatives. In addition, according to the literature, the choice satisfaction is influenced not only by the number of options but also by the perceived variety. The aim of the present study is to apply a novel approach to model perceived variety, in or- der to better understand the perceptions of individuals about the variety of the possible choice options and to model the impact of perceived variety and individuals’ characteristics on the choice outcome satisfaction. We resort to the class of cub (Combination of Uniform and Binomial random variables) models for rating data that model the respondents’ decision process as a combination of two latent components, called feeling and uncertainty , that express, respectively, the level of agreement with the item being evaluated and the human indecision surrounding any discrete choice. The model ap- plied in this paper is an alternative to the most common models used in the studies of human judgments and decisions, whenever attitudes, perceptions and opinions are measured by means of questionnaires having questions with ordered response categories. The chosen approach is composed of two steps: (1) we construct measures of feeling and uncertainty of perceived variety by means of cub and (2) we investigate their impact (eventually together with personal characteristics) on choice satisfaction. The R FastCUB package is exploited to select the best set of covariates to include in the final model.

This paper aims to detect which are the drivers leading to victory for basketball matches in NBA, the American National Basketball Association. First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.

Classifying of skin sensitization using the quantitative structure-activity relationship (QSAR) model is important. Applying descriptor selection is essential to improve the performance of the classification task. Recently, a binary crow search algorithm (BCSA) was proposed, which has been successfully applied to solve variable selection. In this work, a new time-varying transfer function is proposed to improve the exploration and exploitation capability of the BCSA in selecting the most relevant descriptors in QSAR classification model with high classification accuracy and short computing time. The results demonstrated that the proposed method is reliable and can reasonably separate the compounds according to sensitizers or non-sensitizers with high classification accuracy.

We take an in-depth look at the weighted Burr-XII distribution. This distribu-tion generalizes Burr-XII, Lomax, and log-logistic distributions. We discuss the dis-tributional characteristics of the probability density function, the failure rate function,and mean residual lifetime of this distribution. Moreover, we obtain various statisti-cal properties of this distribution such as moment generating function, entropies, meandeviations, order statistics and stochastic ordering. The estimation of the distributionparameters via maximum likelihood method and the observed Fisher information ma-trix are discussed. We further employ a simulation study to investigate the behavior ofthe maximum likelihood estimates (MLEs). A test concerning the existence of size-biasin the sample is provided. In the end, a real data is presented and is analyzed usingthis distribution along with some existing distributions for illustrative purposes.

We consider Bayesian prediction modelling to evaluate a satisfaction index after a first phase of experiment in order to decide to stop or continue at the second stage. We apply this method to Poisson and Gamma distributed outcomes in many fields such as reliability or survival analysis for early termination due to either futility or efficacy. We look at two kinds of decisions making: an hybrid Bayesian-frequentist or a full Bayesian approach.

There is an increasing level of concern about crime and violence in most countries, especially in urban areas. However, data on crime rates have been in a decreasing trend in most countries, especially in the USA. The goal of this paper is to study of the prevalence of violent crimes in Chicago, their spatial neighborhood dependence structure and effect of laws and police enforcement on the rates of crime through time. We analyze thousands of registered cases between 2003 and 2017. In contrast with perceived crime in America, time series data analysis using ARMA models demonstrated that the rates of most violent crimes in Chicago have been decreasing steadily since 2003, and are much lower compared to the beginning of the millennium. The only exception is aggravated assault, which presented a slight increase in the past couple years.

Canonical correlation analysis (CCA) is often used to analyze correlations between the variables of two random vectors. As an extension of CCA, multiple-set canonical correlation analysis (MCCA) was proposed to analyze correlations between multiple-set random vectors. However, sometimes interpreting MCCA results may not be as straightforward as interpreting CCA results. Principal CCA (PCCA), which uses CCA between two sets of principal component (PC) scores, was proposed to address these difficulties in CCA. We propose multiple-set PCCA (MPCCA) by applying the idea to multiple-set of PC scores. PCs are ranked in descending order according to the amount of information they contain. Therefore, it is enough to use only a few PC scores from the top instead of using all PC scores. Decreasing the number of PC makes it easy to interpret the result. We confirmed the effectiveness of MPCCA using simulation studies and a practical example.