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Annals of Data Science OnlineFirst articles

18.08.2018

On the Beta-G Poisson Family

In this article, we propose and study a new family of distributions which is defined by using the genesis of the truncated Poisson distribution and the beta distribution. Some mathematical properties of the new family including moments, quantile …

17.08.2018

Inverse Gompertz Distribution: Properties and Different Estimation Methods with Application to Complete and Censored Data

In this article, we introduce inverse Gompertz distribution with two parameters. Some statistical properties are presented such as hazard rate function, quantile, probability weighted (moments), skewness, kurtosis, entropies function, mean …

16.08.2018

An Alternative Conjugate Prior Distribution for Positive Parameters

In this paper, we propose a new conjugate prior probability distribution to many likelihoods distributions. In particular, we use the weighted Lindley distribution as a conjugate prior distribution. The weighted Lindley distribution can be viewed …

13.08.2018

A Modified Cancelable Biometrics Scheme Using Random Projection

This paper presents a random projection scheme for cancelable iris recognition. Instead of using original iris features, masked versions of the features are generated through the random projection in order to increase the security of the iris …

04.08.2018

The Generalized Burr XII Power Series Distributions with Properties and Applications

We define and study a new family of distributions, called generalized Burr XII power series class, by compounding the generalized Burr XII and power series distributions. Several properties of the new family are derived. The maximum likelihood …

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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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