Nonparametric estimation of aging intensity function for right-censored dependent data

Abstract

Aging Intensity (AI) function is a quantitative measure of hazard function (hazard rate/failure rate), which is used for evaluating the aging behaviour of a component/system. Although variety of research are now available on various properties such as modelling and analysis of AI function; however, a detailed theoretical study on the estimation of the same has not been considered. Accordingly, in the present study, we propose two nonparametric estimators for aging intensity function based on right-censored dependent data scheme and study their properties. Asymptotic properties of the estimators are established under suitable regularity conditions. A simulation study and real data analysis have been carried out to illustrate the performance of the estimators.

Keywords:

• Aging intensity function
• nonparametric density estimation
• α-mixing
• mean squared error (MSE)

AMS CLASSIFICATIONS:

• 62N05
• 60E05

Disclosure statement

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Data availability statements

Authors can confirm that all relevant data are included in the article and/or its supplementary information files.

Additional information

Funding

The second author wish to thank the Science and Engineering Research Board (SERB), Government of India (File No. MTR/2020/000051 vide Diary No. SERB/F/5424/2020-2021 dated 10-12-2020) for the financial support. The third author has been partially supported by PUT under grant 0211/SBAD/0123.