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In this study, Secant Kumaraswamy family of distributions is proposed and studied. This is motivated by the fact that no one distribution can model all types of data from different fields. Therefore, there is the need to develop distributions with desirable properties and flexible enough for modelling data exhibiting different characteristics. Some properties of the new family of distributions, including the quantile function, moments, moment generating function, and mean residual life function, are derived. Five special cases of the family of distributions are presented, and their flexibility is shown by the varying degrees of skewness and kurtosis and nonmonotonic hazard rates. The maximum likelihood estimation method is used to obtain estimators of the family of distributions. Two location-scale regression models are developed for the Secant Kumaraswamy Weibull distribution, which is a special case of the family of distributions. Six different real datasets are used to demonstrate the usefulness of the family of distributions and the regression models. The results show that the family of distributions can be used to model real datasets. [ABSTRACT FROM AUTHOR]

The use of distributions to model and quantify risk is essential in risk assessment and management. In this study, the generalized unit half-logistic geometric (GUHLG) distribution is developed to model bounded insurance data on the unit interval. The corresponding probability density function plots indicate that the related distribution can handle data that exhibit left-skewed, right-skewed, symmetric, reversed-J, and bathtub shapes. The hazard rate function also suggests that the distribution can be applied to analyze data with bathtubs, N-shapes, and increasing failure rates. Subsequently, the inferential aspects of the proposed model are investigated. In particular, Monte Carlo simulation exercises are carried out to examine the performance of the estimation method by using an algorithm to generate random observations from the quantile function. The results of the simulation suggest that the considered estimation method is efficient. The univariate application of the distribution and the multivariate application of the associated regression using risk survey data reveal that the model provides a better fit than the other existing distributions and regression models. Under the multivariate application, we estimate the parameters of the regression model using both maximum likelihood and Bayesian estimations. The estimates of the parameters for the two methods are very close. Diagnostic plots of the Bayesian method using the trace, ergodic, and autocorrelation plots reveal that the chains converge to a stationary distribution. [ABSTRACT FROM AUTHOR]

In this study, two new distributions are developed by compounding Sine-Weibull and zero-truncated geometric distributions. The quantile and ordinary moments of the distributions are obtained. Plots of the hazard rate functions of the distributions show that the distributions exhibit nonmonotonic failure rates. Also, plots of the densities of the distributions show that they exhibit decreasing, skewed, and approximately symmetric shapes, among others. Mixture and nonmixture cure rate models based on these distributions are also developed. The estimators of the parameters of the cure rate models are shown to be consistent via simulation studies. Covariates are introduced into the cure rate models via the logit link function. Finally, the performance of the distributions and the cure rate and regression models is demonstrated using real datasets. The results show that the developed distributions can serve as alternatives to existing models for survival data analyses. [ABSTRACT FROM AUTHOR]

The goal of this research was to use Bayesian switching volatility models to model the stock returns of the GCB, bank in Ghana. Monthly stock prices of GCB bank for the period of 138 months were used for the study. The two-state Markov-Switching GARCH models were used in the study to determine the best model for modelling and forecasting the stock returns. The Deviance Information Criteria was considered when selecting the best model. Based on the Deviance Information Criteria, E-GARCH variance specification with skewed student-t innovation was shown to be appropriate for modelling the stock returns. The estimates of the best model showed the first regime to exhibit the features of "turbulent market conditions" while the second regime exhibits "tranquil market conditions". The risk analysis finds the best model to generally perform better in estimating both Value-at-Risk and Expected Shortfall at 1% rather than 5%. The study advises investors to invest in GCB bank because of the high returns connected with the stock and the fact that when "turbulent market conditions" arise, the recovery rate for these stocks is faster. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new three-parameter distribution defined on the unit interval. The density function of the distribution exhibits different kinds of shapes such as decreasing, increasing, left skewed, right skewed, and approximately symmetric. The failure rate function shows increasing, bathtub, and modified upside-down bathtub shapes. Six different frequentist estimation procedures were proposed for estimating the parameters of the distribution and their performance assessed via Monte Carlo simulations. Applications of the distribution were illustrated by analyzing two datasets and its fit compared to that of other distributions defined on the unit interval. Finally, we developed a regression model for a response variable that follows the new distribution. [ABSTRACT FROM AUTHOR]

This study compared personality risk factors and readiness to change drinking behavior among mandated and volunteer college students. The sample ( N = 583) completed three measures of motivation to change and personality risk factors at baseline, 3 months, and 6 months between 2011 and 2012. Linear mixed models were used to determine an association of continuous outcome variable(s) with covariates over time. Participants in the action stage had lower impulsivity scores. Gender was significant, with females showing the highest anxiety and lowest sensation seeking. The findings indicate a number of future directions to advance innovative alcohol intervention and treatment programs on college campuses. [ABSTRACT FROM AUTHOR]

Introduction: Alcohol consumption and illicit drug use among college students are national public health concerns. Mandated students who have violated campus alcohol policies often are at higher risk for heavy drinking and illicit drug use. Purpose: This longitudinal study compared the effectiveness of the Brief Motivational Intervention (BMI) in decreasing alcohol consumption and illicit drug use, as well as the associated negative consequences among mandated students (Group 1) and voluntary students (Group 2). Methods: Eligible participants (436 voluntary, 147 mandated, N = 583) who agreed to be in the study received the BMI at baseline and again at 2 weeks, with boosters at 3 months and 6 months. Descriptive statistics, frequency tables and summary graphs were used to summarize the study variables in both groups. Results: Alcohol and drug use in both groups decreased between baseline and 6 months, with drug use in the mandated group declining to 10% by 6 months. The consequences, including blackouts, decreased significantly for both groups at baseline and 3 months, and then plateaued at 6 months. Conclusions: The results from this study showed that the BMI was effective in decreasing alcohol consumption and illegal drug use, along with the associated negative consequences, among freshman students and voluntary students who were mandated to attend the program. [ABSTRACT FROM AUTHOR]