A Nonparametric Estimator of Bivariate Quantile Residual Life Model with Application to Tumor Recurrence Data Set

M. Kayid (), M. Shafaei Noughabi () and A. M. Abouammoh ()
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M. Kayid: King Saud University
M. Shafaei Noughabi: University of Gonabad
A. M. Abouammoh: King Saud University

Journal of Classification, 2020, vol. 37, issue 1, No 14, 237-253

Abstract: Abstract Recently, Shafaei and Kayid (Statistical Papers, 2017) introduced and studied the bivariate quantile residual life model. It has been shown that two suitable bivariate quantile residual life functions characterize the underlying distribution uniquely. In the current investigation, we first propose a nonparametric estimator of this new model. The estimator is strongly consistent and, on proper normalization, asymptotically follows a bivariate Gaussian process. An extensive simulation study has been conducted to discuss the behavior of the estimator. Finally, to illustrate the applications, a real data set related to a tumor recurrence trial is presented and discussed.

Keywords: Partial empirical quantile process; Bivariate empirical survival process; Weak convergence; Bivariate normal distribution; 60E15; 62N05 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00357-018-9300-z

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