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Annals of Data Science

Annals of Data Science OnlineFirst articles


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 …


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 …


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 …


A Primer on a Flexible Bivariate Time Series Model for Analyzing First and Second Half Football Goal Scores: The Case of the Big 3 London Rivals in the EPL

The ranking of some English Premier League (EPL) clubs during football season is of keen interest to many stakeholders with special attention to the London rivals: Arsenal, Chelsea and Tottenham. In particular, the first (GF) and second half (GS) …


Treatment Effect Decomposition and Bootstrap Hypothesis Testing in Observational Studies

Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly …

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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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