Preprint Article Version 1 This version is not peer-reviewed

Statistical Models in Comparative Optinalysis through Induced Systematic Skewization Mechanisms

Version 1 : Received: 12 November 2019 / Approved: 13 November 2019 / Online: 13 November 2019 (03:46:03 CET)

How to cite: Abdullahi, K.B. Statistical Models in Comparative Optinalysis through Induced Systematic Skewization Mechanisms. Preprints 2019, 2019110141 (doi: 10.20944/preprints201911.0141.v1). Abdullahi, K.B. Statistical Models in Comparative Optinalysis through Induced Systematic Skewization Mechanisms. Preprints 2019, 2019110141 (doi: 10.20944/preprints201911.0141.v1).

Abstract

The effect of sensitivity points (sequence order and position of every element) of sequences following comparative optinalysis under two proposed mechanisms, the paranodic and synodic skewization, was studied to develop a modeling approach to comparative optinalysis of sequences. The results show that the outcomes of comparative optinalysis (similarity measurement) in a set of paranodically skewed sequences can be modeled deterministically by suitable line regression functions. The sensitivity points (nodes) of a sequence display two important and distinct zones, the K-zones and the B-zones. Continues paranodic skewization within these zones (KB zones) operates within probability space, but at hyperskewization level and at the K-zones only, the outcomes of comparative optinalysis operate outside the probability space. Moreover, the outcomes of comparative optinalysis by synodic skewization can be modeled deterministically by some regression line functions, but a general regression function cannot be identified. At certain limit of skewization value space, following paranodic and synodic skewization, the outcomes of comparative optinalysis at the left-sided sequence form a similar pattern with the right-sided sequence.

Subject Areas

sensitivity points; paranodic skewization; synodic skewization; Kabirian coefficient; regression lines; statistical models

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.