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Annals of Data Science

Annals of Data Science OnlineFirst articles

20.02.2019

On Exponential Negative-Binomial-X Family of Distributions

This paper introduces a new family of distributions using exponential negative binomial distribution. The proposed family of distributions generalizes the Marshall–Olkin, Complementary exponential G-geometric, Complementary Beta G-geometric and …

24.11.2018

On Three Parameter Discrete Generalized Inverse Weibull Distribution: Properties and Applications

In this paper, a new discrete version of generalized inverse Weibull distribution is proposed using the general approach of discretization. Structural properties of the newly introduced discrete model have been discussed comprehensively.

16.11.2018

On the Inverse Power Lomax Distribution

We introduce and study a new three-parameter lifetime distribution named as the inverse power Lomax. The proposed distribution is obtained as the inverse form of the power Lomax distribution. Some statistical properties of the inverse power Lomax …

03.10.2018

Field Assignment, Field Choice and Preference Matching of Ethiopian High School Students

We examined the determinants of the admittance of students into their top wished-fields of study by university students using data from Ethiopian National Educational Assessment and Examination Agency. It is based on a 2016 cohort of 41,371 …

22.09.2018

A New Generalization of the Extended Exponential Distribution with an Application

We introduce a new lifetime distribution namely, transmuted extended exponential distribution which generalizes the extended exponential distribution proposed by Nadarajah and Haghighi (Statistics 45:543–558, 2011) with an additional parameter …

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Über diese Zeitschrift

Annals of Data Science (AODS) is a new academic journal focusing on Big Data analytics and applications. It not only promotes how to use interdisciplinary techniques, including statistics, artificial intelligence and optimization, to process Big Data and conduct data mining, but also how to use the knowledge gleaned from Big Data for real-life applications. AODS accepts high-quality contributions on the foundations of data science, technical papers on various challenging problems in Big Data and meaningful case studies concerning business analytics in the context of Big Data.

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