References
- Jiang R, Ji P, Xiao X. Aging property of unimodal failure rate models. Reliab Eng Syst Saf. 2003;79(1):113–116. doi:10.1016/S0951-8320(02)00175-8
- Nanda AK, Bhattacharjee S, Alam SS. Properties of aging intensity function. Stat Probab Lett. 2007;77(4):365–373. doi:10.1016/j.spl.2006.08.002
- Sunoj SM, Rasin RS. A quantile-based study on ageing intensity function. Commun Stat Theory Methods. 2018;47(22):5474–5484. doi:10.1080/03610926.2017.1395049
- Szymkowiak M. Measures of ageing tendency. J Appl Probab. 2019;56(2):358–383. doi:10.1017/jpr.2019.28
- Bhattacharjee S, Nanda AK, Misra SK. Inequalities involving expectations to characterize distributions. Stat Probab Lett. 2013;83(9):2113–2118. doi:10.1016/j.spl.2013.05.022
- Bhattacharjee S, Nanda AK, Misra SK. Reliability analysis using ageing intensity function. Stat Probab Lett. 2013;83(5):1364–1371. doi:10.1016/j.spl.2013.01.016
- Misra SK, Bhattacharjee S. A case study of aging intensity function on censored data. Alex Eng J. 2018;57(4):3931–3952. doi:10.1016/j.aej.2018.03.009
- Szymkowiak M. Characterizations of distributions through aging intensity. IEEE Trans Reliab. 2018;67(2):446–458. doi:10.1109/TR.2018.2817739
- Szymkowiak M. Aging intensities vector for bivariate absolutely continuous distributions. In: Lifetime analysis by aging intensity functions. Springer; 2020. p. 51–63.
- Rosenblatt M. A central limit theorem and a strong mixing condition. Proc Natl Acad Sci USA. 1956;42(1):43–47. doi:10.1073/pnas.42.1.43
- Withers C. Conditions for linear processes to be strong-mixing. Probab Theory Relat Fields. 1981;57(4):477–480.
- Tjøstheim D, Auestad BH. Nonparametric identification of nonlinear time series: projections. J Am Stat Assoc. 1994;89(428):1398–1409.
- Cai Z. Asymptotic properties of Kaplan–Meier estimator for censored dependent data. Stat Probab Lett. 1998;37(4):381–389. doi:10.1016/S0167-7152(97)00141-7
- Cai Z. Kernel density and hazard rate estimation for censored dependent data. J Multivar Anal. 1998;67(1):23–34. doi:10.1006/jmva.1998.1752
- Fakoor V. Strong uniform consistency of kernel density estimators under a censored dependent model. Stat Probab Lett. 2010;80(5-6):318–323. doi:10.1016/j.spl.2009.11.005
- Sreejith TB, Sunoj SM, Rajesh G. Non-parametric estimation of reciprocal coordinate subtangent for right censored dependent scheme. Commun Stat Theory Methods. 2018;48(13):3177–3190. doi:10.1080/03610926.2018.1473610
- Nicholas CP. Taylor's theorem in a first course. Am Math Mon. 1951;58(8):559–562.
- Van Keilegom I, Veraverbeke N. Hazard rate estimation in nonparametric regression with censored data. Ann Inst Stat Math. 2001;53(4):730–745. doi:10.1023/A:1014696717644
- Cai Z, Roussas GG. Uniform strong estimation under α-mixing, with rates. Stat Probab Lett. 1992;15(1):47–55. doi:10.1016/0167-7152(92)90284-C
- Lawrance AJ, Lewis PAW. The exponential autoregressive-moving average earma (p, q) process. J R Stat Soc Ser B (Methodol). 1980;42(2):150–161.
- Doukhan P. Mixing: properties and examples. Vol. 85. Berlin: Springer Science & Business Media; 2012.
- Colosimo E, Ferreira F, Oliveira M, et al. Empirical comparisons between Kaplan–Meier and Nelson–Aalen survival function estimators. J Stat Comput Simul. 2002;72(4):299–308. doi:10.1080/00949650212847
- Lee ET, Wang J. Statistical methods for survival data analysis. Vol. 476. New York: John Wiley & Sons; 2003.
- D'Agostino RB, Stephens MA. Goodness-of-fit techniques. New York: Marcel Dekker, Inc.; 1986.