An Improved Data Generalization Model for Real-Time Data Analysis

被引：0

作者：

Srisaila, A. ^{[1
]}

Rajani, D. ^{[2
]}

Madhavi, M. V. D. N. S. ^{[2
]}

Jaya Lakshmi, G. ^{[1
]}

Amarendra, K. ^{[3
]}

Dasari, Narasimha Rao ^{[4
]}

机构：

[1] VR Siddhartha Engn Coll, Dept Informat Technol, Vijayawada, India

[2] VR Siddhartha Engn Coll, Dept Math, Vijayawada, India

[3] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vijayawada, India

[4] Def Univ, Coll Engn, Dept Elect Power Engn, Bishoftu, Ethiopia

来源：

SCIENTIFIC PROGRAMMING | 2022年 / 2022卷

关键词：

DISTRIBUTIONS; FAMILY;

D O I：

10.1155/2022/4118371

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This research proposes a maximum likelihood-Weibull distribution (WD) model for the generalized data distribution family. The distribution function of the anticipated maximum likelihood-Weibull distribution is defined where the statistical properties are derived. The data distribution is capable of modelling monotonically decreasing, increasing, and constant hazard rates. The proposed maximum likelihood-Weibull distribution is used for evaluated these parameters. The experimentation is done to evaluate the potential of the maximum likelihood-Weibull distribution estimated. Here, the online available dataset is adopted for computing the anticipated maximum likelihood-Weibull distribution performance. The outcomes show that the anticipated model is well-suited for computation and compared with other distributions as it possesses maximal and least value of some statistical criteria.

引用

页数：9

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