Skip to main content

Annals of Data Science OnlineFirst articles


Inferences Based on Correlated Randomly Censored Gumbel’s Type-I Bivariate Exponential Distribution

The formal random censoring plan has been extensively studied earlier in statistical literature by numerous researchers to deal with dropouts or unintentional random removals in life-testing experiments. All of them considered failure time and …


Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh’s 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and …


Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series

In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in …


Exchange Rate Forecasting: Nonlinear GARCH-NN Modeling Approach

This paper targets the description of the fusion of modeling techniques, such as the GARCH model and the Artificial Neural Network (ANN), for the sake of predicting financial series and precisely the series of the exchange rate in Tunisia, namely …


An Alternative to the Beta Regression Model with Applications to OECD Employment and Cancer Data

In regression analysis involving response variable on the bounded unit interval [0, 1], the beta regression model often suffice as a common choice, however, there are many alternatives to the beta regression model. In this article, we add yet …

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.

Annals of Data Science
Jahrgang 1/2014 - Jahrgang 10/2023
Springer Berlin Heidelberg
Elektronische ISSN
Print ISSN

Premium Partner