Analyzing Non-Monotonic Hazard Rate Data Using the Harris Extended Inverted Lindley Distribution

Analyzing Non-Monotonic Hazard Rate Data Using the Harris Extended Inverted Lindley Distribution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing Non-Monotonic Hazard Rate Data Using the Harris Extended Inverted Lindley Distribution Jabir Bengalath, Bindu Punathumparambath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4765614/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this paper, we introduce a new three-parameter extension of the inverse Lindley distribution known as the Harris extended inverted Lindley (HEIL) distribution, which offers more flexibility in modeling upside-down bathtub lifetime data. Some statistical properties of the proposed model were explicitly derived. These include density and hazard rate functions with their behavior, ordinary and incomplete moments, skewness, kurtosis measures, moment generating functions, reliability properties, and the quantile function. The maximum likelihood estimation of the parameters, their estimated asymptotic distribution and confidence intervals were derived. Renyi entropy as a measure of the uncertainty in the model is derived. The simulation study is conducted to evaluate the performance of the maximum likelihood estimator. Finally, to assess the superiority of the proposed model three real data sets were examined. Mathematics Subject Classification (2020) 60E05 · 62E10 · Statistical Theory Inverse Lindley distribution Harris extended inverted Lindley distributions Reliability Properties Maximum likelihood estimation Monte Carlo simulations Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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