An Improved Data Generalization Model for Real-Time Data Analysis

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...

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Bibliographic Details
Published in:Scientific programming 2022-08, Vol.2022, p.1-9
Main Authors: Srisaila, A, Rajani, D, Madhavi, M V D N S, Jaya Lakshmi, G, Amarendra, K, Dasari, Narasimha Rao
Format: Article
Language:English
Subjects:
Data analysis
Datasets
Distribution functions
Experimentation
Failure
Maximum likelihood estimates
Parameter estimation
Random variables
Statistical analysis
Weibull distribution
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Summary: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.
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/4118371