AI Article Synopsis

  • The article presents a new statistical distribution with two tuning parameters, created through a transformation of the Weibull distribution, addressing a gap in research regarding hyperbolic transformations for flexible distributions.
  • It establishes the key properties of this new distribution, outlines various estimation methods, and introduces a quantile regression model aimed at analyzing educational measurements among OECD and non-OECD countries.
  • The quantile regression model demonstrates superior fitting capabilities compared to traditional models when dealing with skewed response variables and significant independent variables.

Article Abstract

This article introduces a new distribution with two tuning parameters specified on the unit interval. It follows from a 'hyperbolic secant transformation' of a random variable following the Weibull distribution. The lack of research on the prospect of hyperbolic transformations providing flexible distributions over the unit interval is a motivation for the study. The main distributional structural properties of the new distribution are established. The different estimation methods and two simulation works have been derived for model parameters. Subsequently, we develop a related quantile regression model for further statistical perspectives. We consider two real data applications based on the educational measurements of both OECD and some non-members of OECD countries. Our regression model aims to relate the desire to get top grades on certain young students in the OECD countries with some of their Education and School Life Index such as reading performance, work environment at home, and paid work experience. It is shown that the elaborated quantile regression model has a better fitting power than famous regression models when the unit response variable possesses skewed distribution as well as two independent variables are significant in the statistical sense at any standard significance level for the median response.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754050PMC
http://dx.doi.org/10.1080/02664763.2021.1981834DOI Listing

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