Recurrence Relations for Single and Product Moments of Generalized Order Statistics from Marshall-Olkin Extended General Class of Distributions

Marshall and Olkin introduced a new method of adding parameter to expand a family of distributions. Using this concept, in this paper the Marshall-Olkin extended general class of distributions is introduced. Further, some recurrence relations for single and product moments of generalized order statistics (gos) are studied. Also the results are deduced for record values and order statistics

Concomitants of Dual Generalized Order Statistics from Farlie Gumbel Morgenstern Type Bivariate Inverse Rayleigh Distribution

Dual generalized order statistics constitute a unified model for descending order random variables, like reverse order statistics and lower record values. In this paper, we have considered concomitants of dual generalized order statistics for the Farlie-Gumbel-Morgenstern type bivariate inverse Rayleigh distribution and single and joint distribution of concomitants of dual generalized order statistics are obtained. Further, Single and product moments are derived and recurrence relations between moments are established. Also results are deduced for order statistics and lower record values and some computation works are carried out

Recurrence Relations for Single and Product Moments of Generalized Order Statistics from Marshall-Olkin Extended General Class of Distributions

Marshall and Olkin introduced a new method of adding parameter to expand a family of distributions. Using this concept, in this paper the Marshall-Olkin extended general class of distributions is introduced. Further, some recurrence relations for single and product moments of generalized order statistics (gos) are studied. Also the results are deduced for record values and order statistics

The Age- And Sex- Specific Burden of Transport Injuries in India Over a Decade From 2010-2019: A Systematic Analysis from Global Burden of Diseases 2019

Background: In India, transport injuries persist as leading preventable causes of mortality and morbidity for a large number of people, including children, young adults and elderly people. The objective is to estimate the transport injury-related mortality and morbidity in India over the past decade from 2010-2019. Methodology: By using the Global Burden of Diseases, Injuries, and Risk Factors 2019 Study (GBD), we analysed mortality, Disability-Adjusted Life-Years (DALYs), Years Lived in Disability (YLDs), Years of Life Lost (YLL), prevalence rate (per 100K) attributed to transport injuries for all ages, in India. Burden is reported in absolute numbers and percentage changes over a decade period from 2010 to 2019; stratified by sex ratio, and age groups, with 95% confidence intervals (CIs). Results: Transport injuries had accounted for 235,444 deaths (2.51%) in 2019; and 231,177 deaths (2.68%) in 2010. Transport injuries are the leading cause of death among people aged 15-49 years with more than 50% of burden in India. Death-rate due to transport had declined from 18.77 to 16.93 per 100,000 populations over a decade (2010-2019). Conclusions: Over a decade, progress made in the burden of transport injuries was limited and the burden had started to rise after achieving some success till 2016. India needs to sustain and improve the progress made in order to achieve UN goals for 2030

Statistical study for Covid-19 spread during the armed crisis faced by Ukrainians

Russia and Ukraine got into an armed conflict on 24th February 2022. In addition, the World Health Organisation still warns of a fast growth in infections and deaths. Infectious disease remains a serious issue in Ukraine and poorly governed cities, such as those in armed conflicts. During this period of security instability, the coronavirus situation in Ukraine is alarming and needs more attention. In this context, our focus in the current work is to model COVID-19 spread risk from Ukrainian international refugees in neighboring countries. This study aims to estimate the number of daily coronavirus cases among Ukrainian international refugees for informed decisions for the pandemics' spread risk. For that reason, we used “Coronavirus Pandemic (COVID-19)” data from “Our World in Data” (from 2020-03-03 to 2022-02-22) and the data about Ukrainian International Refugees provided by United Nations High Commissioner for Refugees related (from 2022-02-22 to 2022-03-11). We performed ARIMA, TBATS, and ETS and selected the best model. Through a cross-validation process, the findings revealed that around 6 individuals [95% CI: 5%–7%] over 10,000 Ukrainian international refugees are likely COVID-19 cases. ARIMA is the best model to fit the Ukrainian daily number of cases among the refugees fleeing the crisis. On average, they are daily 100 possible COVID-19 cases among Ukrainian international refugees and authorities and humanitarian actors need be informed decisions to control the pandemic and support refugees effectively

On modeling the log-returns of Bitcoin and Ethereum prices against the USA Dollar

The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates