English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

At the beginning of the xix century, K. Gauss published several fundamental works, which introduced, among other things, a series of important statistical concepts such as “a least-square method”, “maximum likelihood”, as well as substantiated a “normal” distribution. This distribution law, often referred to as Gaussian, is by far the most common technique of probabilistic description. However, based on a series of theoretical limitations, it does not describe all possible variety of statistics. The axiomatic statement underlying Gauss’s approach was slightly altered at the beginning of the 20th century in work [1], where a more general law for the distribution of errors was justified, termed as an exponential power distribution (EPD). This probabilistic model is better suited to describe a larger number of phenomena that can be observed in reality. Its versatility is evidenced by the fact that the Laplace distribution, Gaussian distribution, and uniform distributions are separate cases of EPD at certain values of its form parameter. Therefore, various literary sources [2‒5] use alternative designations such as the “generalized normal distribution”, “generalized Gaussian distribution”, “generalized distribution of errors”, “Subbotin distribution”, etc. In addition, the family of exponential power distributions includes more complex multimodal [6] or asymmetrical [7, 8] modifications. Copyright © 2021, S. Zabolotnii, V. Chotunov, A. Chepynoha, O. Tkachenko

Estimating parameters of linear regression with an exponential power distribution of errors by using a polynomial maximization method