Classical and Bayesian inference for the discrete Poisson Ramos-Louzada distribution with application to COVID-19 data.

The present study is based on the derivation of a new extension of the Poisson distribution using the Ramos-Louzada distribution. Several statistical properties of the new distribution are derived including, factorial moments, moment-generating function, probability moments, skewness, kurtosis, and dispersion index. Some reliability properties are also derived. The model parameter is estimated using different classical estimation techniques. A comprehensive simulation study was used to identify the best estimation method. Bayesian estimation with a gamma prior is also utilized to estimate the parameter. Three examples were used to demonstrate the utility of the proposed model. These applications revealed that the PRL-based model outperforms certain existing competing one-parameter discrete models such as the discrete Rayleigh, Poisson, discrete inverted Topp-Leone, discrete Pareto and discrete Burr-Hatke distributions.

The Influence of Social Media on the Decision of Rhinoplasty Among Adults in the Western and Southern Regions of Saudi Arabia: A Comparative Cross-Sectional Study.

Introduction Rhinoplasty, a cosmetic surgical procedure aimed at altering the appearance of the nose, has gained immense popularity worldwide. Patients undergo this procedure for various reasons, ranging from aesthetic concerns to functional impairments. Social media, being a ubiquitous platform for sharing and consuming visual content, has emerged as a potential influencer for individuals contemplating rhinoplasty. This study aims to investigate the impact of social media on the prevalence of rhinoplasty among individuals residing in the southern and western regions of Saudi Arabia. Methods A cross-sectional study was conducted through an online self-administered questionnaire, targeting male and female adults aged 18 years or older, residing in the western and southern regions of Saudi Arabia. The questionnaire comprised 17 questions, categorized into two sections. The first section sought demographic information, including age, gender, education, and other relevant characteristics. The second section focused on the influence of social media on the decision-making process related to rhinoplasty. Results A total of 1645 participants responded to the survey, with 96.80% being Saudi citizens. The majority of respondents were females (69.11%); 58.52% of the respondents were from the western region of Saudi Arabia, while 41.48% lived in the southern region. Most participants (64.27%) were aged between 18 and 30 years. The study revealed that Snapchat (Snap Inc., Santa Monica, California, United States) was the most influential social media platform, with 43.41% of respondents reporting it as the key influencer for their decision to undergo rhinoplasty. Twitter (Twitter, Inc., San Francisco, California, United States) and Instagram (Meta Platforms, Inc., Menlo Park, California, United States) followed at 22.97% and 12.09%, respectively. Interestingly, 28.42% of respondents acknowledged that social media played a significant role in their decision to undergo rhinoplasty, particularly when promoted by celebrities or trusted figures. Comparing responses from the western and southern regions, the study showed that individuals from the southern region were relatively more influenced by social media, with 27.8% and 29.3% of respondents reporting the influence from the two regions, respectively. Out of the total respondents, only 38.75% reported dissatisfaction with their nose's appearance and condition, while 23.60% expressed a tendency towards undergoing rhinoplasty. Conclusion The study's findings underscore the critical role of social media in influencing patients' decisions to undergo rhinoplasty, particularly in the southern region of Saudi Arabia. Snapchat emerged as the most influential social media platform, with celebrities' pictures before and after the procedure being the leading factor in motivating patients to undergo rhinoplasty. The study highlights the need for further research to explore the potential risks and benefits associated with the influence of social media on patients' decision-making regarding rhinoplasty.

Parameter Estimation in Step Stress Partially Accelerated Life Testing under Different Types of Censored Data.

A long testing period is usually required for the life testing of high-reliability products or materials. It is possible to shorten the testing process by using ALTs (accelerated life tests). Due to the fact that ALTs test products in harsher settings than are typical use conditions, the life expectancy of the objects they evaluate is reduced. Censored data in which the specific failure timings of all units assigned to test are not known, or all units assigned to test have not failed, may arise in ALTs for a variety of reasons, including operational failure, device malfunction, expense, and time restrictions. In this paper, we have considered the step stress partially accelerated life test (SSPALT) under two different censoring schemes, namely the type-I progressive hybrid censoring scheme (type-I PHCS) and the type-II progressive censorship scheme (type-II PCS). The failure times of the items are assumed to follow NH distribution, while the tampered random variable (TRV) model is used to explain the effect of stress change. In order to obtain the estimates of the unknown parameters, the maximum likelihood estimation (MLE) approach is adopted. Furthermore, based on the asymptotic theory of MLEs, the approximate confidence intervals (ACIs) are also constructed. The point estimates under two censoring schemes are compared in terms of root mean squared errors (RMSEs) and relative absolute biases (RABs), while ACIs are compared in terms of their lengths and coverage probabilities (CPs). The performance of the estimators has been evaluated and compared under two censoring schemes with various sample sizes through a simulation study. Simulation results show that estimates with type-I PHCS outperform estimates with type-II PCS in terms of RMSEs, RABs, lengths, and CPs. Finally, a real-world numerical example of insulating fluid failure times is presented to show how the approaches will work in reality.

Brand Awareness via Online Media: An Evidence Using Instagram Medium with Statistical Analysis.

Online marketing refers to the practices of promoting a company's brand to its potential customers. It helps the companies to find new venues and trade worldwide. Numerous online media such as Facebook, YouTube, Twitter, and Instagram are available for marketing to promote and sell a company's product. However, in this study, we use Instagram as a marketing medium to see its impact on sales. To carry out the computational process, the approach of linear regression modeling is adopted. Certain statistical tests are implemented to check the significance of Instagram as a marketing tool. Furthermore, a new statistical model, namely a new generalized inverse Weibull distribution, is introduced. This model is obtained using the inverse Weibull model with the new generalized family approach. Certain mathematical properties of the new generalized inverse Weibull model such as moments, order statistics, and incomplete moments are derived. A complete mathematical treatment of the heavy-tailed characteristics of the new generalized inverse Weibull distribution is also provided. Different estimation methods are discussed to obtain the estimators of the new model. Finally, the applicability of the new generalized inverse Weibull model is established via analyzing Instagram advertising data. The comparison of the new distribution is made with two other models. Based on seven analytical tools, it is observed that the new distribution is a better model to deal with data in the business, finance, and management sectors.

The Role of Twitter Medium in Business with Regression Analysis and Statistical Modelling.

Marketing refers to the strategies a company undertakes to promote its brands to its potential audience. Advertising provides useful venues for marketing to promote a company's survives/goods to the audience. It has a positive impact on the sale of services or products. In this study, we consider a well-known online medium called Twitter (the fourth most popular social media platform used by marketers) to check its impact on sales. For this purpose, the simple linear regression modeling approach is implemented to test the significance and usefulness of Twitter advertising on sale. Statistical tests such as t-test and correlation test are adopted to test the hypothesis of the "impact of Twitter advertising on sales." Based on the findings of this study, it is observed that Twitter advertising has a positive impact on sales. Furthermore, a new statistical model called the exponential T-X exponentiated exponential is introduced. The proposed model is very interesting and possesses heavy-tailed characteristics which are useful in finance and other related sectors. Finally, the applicability of the new model is illustrated by considering the sales data.

Impact of YouTube Advertising on Sales with Regression Analysis and Statistical Modeling: Usefulness of Online Media in Business.

Computer technology plays a prominent role in almost every aspect of daily life including education, health care, online shopping, advertising, and even in homes. Computers help to make daily tasks much easier and convenient. Among social media, YouTube is a well-known social sharing networking service. As more and more people join social media and become everyday users, brands have also increased their online engagement. However, it is still unclear how to effectively measure value and return on advertising using social media. As of 2021, more than 31 million YouTube channels around the globe have been opened. In this paper, we consider YouTube advertising to check its effectiveness and benefits gained. Certain statistical tools are adopted to measure the extent of advertising benefits and their correlation in creating effective advertising campaigns on YouTube. Simple linear regression analysis is performed on the data representing the YouTube advertising budget of a company and the sales data of that company. Furthermore, we develop a new statistical distribution to provide the best description of the YouTube advertising data. The result of this research shows that YouTube is an effective medium for advertising and has a strong relationship with sales.

Quantifying conditional probability tables in Bayesian networks: Bayesian regression for scenario-based encoding of elicited expert assessments on feral pig habitat.

Bayesian networks are now widespread for modelling uncertain knowledge. They graph probabilistic relationships, which are quantified using conditional probability tables (CPTs). When empirical data are unavailable, experts may specify CPTs. Here we propose novel methodology for quantifying CPTs: a Bayesian statistical approach to both elicitation and encoding of expert-specified probabilities, in a way that acknowledges their uncertainty. We illustrate this new approach using a case study describing habitat most at risk from feral pigs. For complicated CPTs, it is difficult to elicit all scenarios (CPT entries). Like the CPT Calculator software program, we ask about a few scenarios (e.g. under a one-factor-at-a-time design) to reduce the experts' workload. Unlike CPT Calculator, we adopt a global rather than local regression to 'fill out' CPT entries. Unlike other methods for scenario-based elicitation for regression, we capture uncertainty about each probability in a sequence that explicitly controls biases and enhances interpretation. Furthermore, to utilize all elicited information, we introduce Bayesian rather than Classical generalised linear modelling (GLM). For large CPTs (e.g. >3 levels per parent) we show Bayesian GLM supports richer inference, particularly on interactions, even with few scenarios, providing more information regarding accuracy of encoding.