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\\begin{document} \\baselineskip=24pt \\begin{center}

{\\Large \\bf Nonparametric Inference for

the Stress--Strength Model under Right Censoring} \\end{center}

\\begin{center} {\\bf Hui Qi} \\\\

Institute of Information Engineering, Sanming University\\\\

Sanming, Fujian 365004, China \\\\ {{\\it email}: qh@smxy.cn} \\end{center}

\\begin{center}

{\\bf Jichang Yu and Qinglong Yang$^{*}$} \\\\

School of Statistics and Mathematics, Zhongnan University of Economics and Law \\\\

Wuhan, Hubei 430073, China \\\\

{{\\it email}: yjichang@gmail.com and qinglongyang@hotmail.com}

\\end{center}

\\footnotetext{$^{*}$Correspondence:Dr. Yang Qinglong, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073,

China. E-mail: qinglongyang@hotmail.com. Supported by National Natural Science Foundation of China (Grant No. 11301545)} \\begin{center} {\\bf Abstract} \\end{center}

The stress--strength model is widely applied in reliability. Observations

are often subject to right censoring due to some practical limitations.

In such circumstance, the statistical inference for the stress--strength model is demanding, although lacking. We propose a nonparametric method for

the inference of the stress--strength model when the observations

are subject to right censoring. The asymptotic properties are also established.

The practical utility of the proposed method is assessed through both simulated and real data sets.

\\vspace{0.5cm} \\noindent{\\bf KEY WORDS}: Reliability; right censoring;

the Kaplan--Meier estimator; the stress--strength model. \\end{document}