In this paper, we obtain bounds for the population coefficient of variation (CV) in Bernoulli, Discrete Uniform, Normal and Exponential distributions. We also show that the sample coefficient of variation (cv) is not an accurate estimator of the population CV in the above indicated...

This book provides a broad introduction to computational aspects of Singular Spectrum Analysis (SSA) which is a non-parametric technique and requires no prior assumptions such as stationarity, normality or linearity of the series. This book is unique as it not only details the theoretical...

In this article we introduce an approximately unbiased estimator for the population coefficient of variation, τ, in a normal distribution. The accuracy of this estimator is examined by several criteria. Using this estimator and its variance, two approximate confidence intervals for τ are...

The characteristic function of the folded normal distribution and its moment function are derived. The entropy of the folded normal distribution and the Kullback–Leibler from the normal and half normal distributions are approximated using Taylor series. The accuracy of the results are also...

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely,...

Retrieving valuable knowledge and statistical patterns from official data has a great potential in supporting strategic policy making. Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we provide an introduction...

Purpose: Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of...