Divide and Recombine Approach for Analysis of Failure Data Using Parametric Regression Model

Md. Razanmiah (), Md. Kamrul Islam () and Md. Rezaul Karim ()
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Md. Razanmiah: University of Rajshahi, Department of Statistics
Md. Kamrul Islam: University of Rajshahi, Department of Statistics
Md. Rezaul Karim: University of Rajshahi, Department of Statistics

A chapter in Data Science and SDGs, 2021, pp 55-65 from Springer

Abstract: Abstract The failure data of some products depend on factors or covariates such as the operating environment, usage conditions, etc. Under this situation, the parametric regression model is applied for modeling the failure data of the product as a function of covariates. Divide and recombine (D&R) is a new statistical approach to the analysis of big data. In the D&R approach, the data are divided into manageable subsets, an analytic method is applied independently to each subset, and the outputs are recombined. This chapter applies the D&R approach for analysis of an automobile component failure data using the Weibull regression model. Extensive simulation studies are presented to evaluate the performance of the proposed methodology with comparison to the traditional statistical estimation method.

Keywords: Big data; Divide and recombine (D&R) approach; Failure data; Weibull regression model (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1919-9_5

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DOI: 10.1007/978-981-16-1919-9_5

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