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Annals of Data Science

Annals of Data Science OnlineFirst articles


Inferences for the DUS-Exponential Distribution Based on Upper Record Values

This article considers the problem of estimation of the parameter of DUS-exponential distribution based on upper records through classical as well as Bayesian procedures. Maximum likelihood estimator is calculated under the classical scheme and …


Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market

The stock market is a popular investment option for investors because of its expected high returns. Stock market prediction is a complex task to achieve with the help of artificial intelligence. Because stock prices depend on many factors …


Bayesian Survival Analysis of Type I General Exponential Distributions

This article aims at generalizing two distribution by means of, exponentiated exponential and Weibull distribution. The researchers have employed three and four parameters life model called the Type I General Exponential exponentiated exponential …


A Simple Extension of Burr-III Distribution and Its Advantages over Existing Ones in Modelling Failure Time Data

In this article we consider a four parameter extended Burr-III distribution and study some distributional, reliability properties and parameter estimation. Performance of estimation technique used for model parameters estimation is numerically …


Marshall–Olkin Alpha Power Inverse Exponential Distribution: Properties and Applications

In this paper, we use the method of the Marshall Olkin alpha power transformation to introduce a new generalized Marshall Olkin alpha power inverse exponential (MOAPIE) distribution. Its characterization and statistical properties are obtained …

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About this journal

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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