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Annals of Data Science OnlineFirst articles


Alpha-Power Transformed Lindley Distribution: Properties and Associated Inference with Application to Earthquake Data

The Lindley distribution has been generalized by many authors in recent years. A new two-parameter distribution with decreasing failure rate is introduced, called Alpha Power Transformed Lindley (APTL, in short, henceforth) distribution that …


A New Generalized Class of Distributions: Properties and Estimation Based on Type-I Censored Samples

This article introduces a new generalized family of distributions, which is a generalization of the exponentiated and transmuted family of distributions. A special model of this family, namely, new generalized Weibull distribution is considered in …


A Note on Modeling the Maxima of Lagos Rainfall

The Lagos annual maximum rainfall is modeled by the generalized extreme value distribution. Hydrologic risk measures like the probability of exceedance or recurrence, return period, and return level is given.


Histopathological Breast-Image Classification Using Concatenated R–G–B Histogram Information

Breast Cancer is a serious threat to women. The identification of breast cancer relies heavily on histopathological image analysis. Among the different breast-cancer image analysis techniques, classifying the images into Benign and Malignant …


Exponentiated Power Lindley Logarithmic: Model, Properties and Applications

A new class of lifetime distributions is proposed. Closed form expressions are provided for the density, cumulative distribution, survival and hazard rate functions. Maximum likelihood estimation is discussed and formulas for the elements of the …

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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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