A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test

A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test

The presence of missing data is a significant data quality issue that negatively impacts the accuracy and reliability of data analysis. This issue is especially relevant in the context of accelerated tests, particularly for step-stress accelerated degradation tests. While missing data can occur due...

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Bibliographic Details
Main Authors: Yaqiu Li, Qijie Zhou, Ye Fan, Guangze Pan, Zongbei Dai, Baimao Lei
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:Heliyon
Subjects:
Missing data imputation
Accelerated degradation test
Support vector machine
Radial basis function
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024024605

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