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This research article introduces a novel family of distributions achieved through the methodology of the π-power transformation technique. The study focuses on one specific member that is inverse Weibull distribution within this family, which showcases a hazard function exhibiting distinct J, reverse-J, bathtub, or monotonically increasing shapes. The article explores the essential characteristics of this distribution and employs the maximum likelihood estimation (MLE) method to estimate its associated parameters. To evaluate the accuracy of the estimation procedure, a simulation experiment is conducted, revealing a decrease in biases and mean square errors as sample sizes increase, even when working with small samples. Furthermore, the practical application of the proposed distribution is demonstrated by analyzing two real medical and traffic datasets. By employing model selection criteria and conducting goodness-of-fit test statistics, the article establishes that the proposed model surpasses existing models in performance. The application of this research work can be significant in various fields where modeling and analyzing hazard functions or survival data are essential, while also making contributions to probability theory and statistical inferences. [ABSTRACT FROM AUTHOR]

This article is dedicated to the study of the new class of distributions and one of its particular members. Based on the ratio of CDF G(x) and 1+G(x) of the baseline distribution, we have developed the new trigonometric family of distributions by transforming the sine function, and we named it the new class sin-G (NCS-G) family of distributions. The general properties of the suggested family of distributions are provided. Using the inverted Weibull distribution as a baseline distribution, we have introduced a member of the suggested family having a reverse-j or increasing, or inverted bathtub-shaped hazard function. Some statistical properties of this NCS-IW distribution are explored. The associated parameters of the new distribution are estimated through the MLE method. To assess the estimation procedure, we conducted a Monte Carlo simulation and found that even for small samples, biases and mean square errors decreased as the size of the sample increased. Two real medical data sets are considered for the application of the NCS-IW distribution. Using some criteria for model selection and goodness of fit test statistics, we empirically proved that the suggested model performs better than six other existing models (most of which have more parameters). [ABSTRACT FROM AUTHOR]

In this article, we extensively study a family of distributions using the trigonometric function. We add an extra parameter to the sine transformation family and name it the alpha-sine-G family of distributions. Some important functional forms and properties of the family are provided in a general form. A specific sub-model alpha-sine Weibull of this family is also introduced using the Weibull distribution as a parent distribution and studied deeply. The statistical properties of this new distribution are investigated and intended parameters are estimated using the maximum likelihood, maximum product of spacings, least square, weighted least square, and minimum distance methods. For further justification of these estimates, a simulation experiment is carried out. Two real data sets are analyzed to show the suggested model's application. The suggested model performed well compares to some existing models considered in the study. [ABSTRACT FROM AUTHOR]

Background and Aims: Healthcare students are expected to lead healthy lives yet they may engage in health risk behaviors (HRBs) like physical inactivity, poor diet, and substance use. These behaviors can have negative consequences for both the individual's health and well-being, as well as their ability to perform their future roles as healthcare providers. This study aimed to assess the prevalence of HRBs and associated factors among medical and nursing students at Lumbini Medical College, Nepal., Methods: A cross-sectional study was conducted among 412 undergraduate healthcare students using a self-administered questionnaire. HRBs across various domains were assessed. Validated scales screened for anxiety and depression. Regression analyses determined associations between mental health and HRBs., Results: Low physical activity was prevalent, with only 10.7% exercising ≥5 times/week. Inadequate fruit/vegetable intake (1-2 servings/day) was reported by 79.9% students. Short sleep duration (5-6 h) on weeknights (51.2%) was common. More than three quarter (76.5%) of students met the screening cutoff score for HRBs. The study highlighted that students with addiction, depression and anxiety are more likely to exhibit HRBs ( p  < 0.01)., Conclusion: Multiple HRBs were highly prevalent among the students. A considerable proportion demonstrated a clustering of risky lifestyle factors, which were linked to poor mental health. Interventions should address academic burden, promote positive health behaviors, and target mental well-being in this vulnerable group., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s). Health Science Reports published by Wiley Periodicals LLC.)