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This article examines the new inverse unit exponential distribution, utilizing both classical and Bayesian methodologies; it begins by presenting the general properties of the proposed model, highlighting characteristic features, such as the presence of a reverse-J or increasing and inverted bathtub-shaped hazard rate function. Furthermore, it explores various statistical properties of the suggested distribution. It employs 12 methods to estimate the associated parameters. A Monte Carlo simulation is conducted to evaluate the accuracy of the estimation procedure. Even for small samples, the results indicate that biases and mean square errors decrease as the sample size increases, demonstrating the robustness of the estimation method. The application of the suggested distribution to real datasets is then discussed. Empirical evidence, using model selection criteria and goodness-of-fit test statistics, supports the assertion that the suggested model outperforms several existing models considered in the study. This article extends its analysis to the Bayesian framework. Using the Hamiltonian Monte Carlo with the no U-turn sampling algorithm, 8000 real samples are generated. The convergence assessment reveals that the chains are convergent and the samples are independent. Subsequently, using the posterior samples, the parameters of the proposed model are estimated, and credible intervals and highest posterior density intervals are computed to quantify uncertainty. The applicability of the suggested model to real data under both classical and Bayesian methodologies provides insights into its statistical properties and performance compared to existing models. [ABSTRACT FROM AUTHOR]

In this research, we establish the novel sin-G class, an innovative and comprehensive class of trigonometric distributions centered on the sine function. A sin-G class analytical approach comprises calculations for the probability density function and reliability indicators. Various estimation methods were presented to estimate the specified model parameters. Applying randomly created datasets and the provided estimating techniques, the behavior of these variables was investigated. Then, the focus is directed toward a specific member of this class, the novel sin-Lomax distribution, which is established with the Lomax distribution serving as the baseline. Exploring the innovative sin-Lomax distribution's inferential characteristics, the constructed distribution is then fitted to real datasets, demonstrating the actual application of the Sin-G class. [ABSTRACT FROM AUTHOR]

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 research article introduces a new family of distributions developed using the innovative beta-generated transformation technique. Among these distributions, the focus is on the inverse exponential power distribution, which exhibits unique reverse-J, inverted bathtub, or monotonically increasing hazard functions. This paper thoroughly investigates the distribution's key characteristics and utilizes the maximum likelihood estimation method to determine its associated parameters. To assess the accuracy of the estimation procedure, the researchers conducted a simulation experiment, revealing diminishing biases and mean square errors with increasing sample sizes, even when working with small samples. Moreover, the practical applicability of the proposed distribution is demonstrated by analyzing real-world COVID-19 and medical datasets. The article establishes that the proposed model outperforms existing models by using model selection criteria and conducting goodness-of-fit test statistics. The potential applications of this research extend to various fields where modeling and analyzing hazard functions or survival data are crucial. Additionally, the study contributes to advancing 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]

This article aims to introduce the inverse new XLindley distribution, a further extension of the new XLindley distribution. The article explores various properties of the proposed model, such as the quantile function, stochastic orders, entropies, fuzzy reliability, moments, and stress–strength estimation. The paper also compares different methods of estimating the parameters of the proposed model and evaluates their performance using a simulation study. Moreover, the paper demonstrates the usefulness of the proposed model by applying it to two real datasets. The article shows that the proposed model fits the data better than seven existing models based on model selection criteria, goodness-of-fit test statistics, and graphical visualizations. The paper concludes that the new model can be a valuable tool for modeling and analyzing hazard functions or survival data in various fields and contributing to probability theory and statistical inferences. [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]

We develop a new three parameter distribution using the half-Cauchy family of distributions, called the half-Cauchy generalized Rayleigh distribution. Some statistical properties and characteristics of the proposed distribution are provided and obtained, including the explicit expressions for the survival function, median, hazard function, mode, moments, mean deviation, order statistics, cumulative hazard function, quantiles, and the measures of dispersion based on quartiles and octiles. For the parameter estimation of the proposed model, three widely used estimation techniques namely, maximum likelihood estimators (MLE), Cramer-Von-Mises (CVM), and least-square estimation (LSE) methods are applied. Two real datasets are used for the illustration, and the goodness-of-fit test is run. It is discovered that the proposed model fits the real data very well and is more flexible than some well-known models under consideration. [ABSTRACT FROM AUTHOR]

Based on the power Cauchy distribution, we have purposed a new distribution called inverse power Cauchy distribution that offers greater modeling flexibility for lifetime data. Real-world data can be efficiently analyzed using the suggested model because it is analytically sound. Its density function can take on a number of different shapes, including reversed-J, symmetrical, and right-skewed. Depending on the different values of the parameters, it can adapt to different hazard forms, such as an upside-down bathtub, a monotonically increasing or decreasing curve, and others. Its moments, quantile, reliability, hazard, order statistics with density function, moment generating function, and entropy are all given with various explicit forms. The observed information matrix is created when the new model's parameters are calculated through maximum likelihood technique. A simulation study is conducted to investigate the behaviour of maximum likelihood estimators. The proposed model gets a superior fit compared to certain well-known distributions, according to the test of goodness-of-fit we conducted. The significance of the purposed distribution is demonstrated empirically using two real-world data sets. [ABSTRACT FROM AUTHOR]

This paper proposes the 4-parameter odd Lomax generalized exponential distribution for the study of engineering and COVID-19 data. The statistical and mathematical properties of this distribution such as a linear representation of the probability density function, survival function, hazard rate function, moments, quantile function, order statistics, entropy, mean deviation, characteristic function and average residual life function are established. The estimates of parameters of the proposed distribution are obtained using maximum likelihood estimation (MLE), Maximum product spacings (MPS), least-square estimation (LSE), and Cramer-Von-Mises estimation (CVME) methods. A Monte-Carlo simulation experiment is carried out to study the performance of MLEs. The applicability of the proposed distribution is evaluated using two real datasets related to engineering and COVID-19 death cases. All the computational work was performed in R programming software. [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.)