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An Invariance Principle for Discontinuity Estimation in Smooth Hazard Functions under Random Censoring
Hans-Georg Müller and Jane-Ling Wang
Sankhyā: The Indian Journal of Statistics, Series A (1961-2002)
Vol. 58, No. 3 (Oct., 1996), pp. 392-402
Published by: Indian Statistical Institute
Stable URL: http://www.jstor.org/stable/25051118
Page Count: 11
You can always find the topics here!Topics: Estimators, Mathematical discontinuity, Censorship, Statism, Statistical estimation, Kernel functions, Censored data, Data smoothing, Goodbyes, Confidence interval
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Consider a hazard function which is smooth with the exception of a discontinuity or a discontinuity in its ν-th derivative. Nonparametric estimators for the discontinuity location are constructed by maximizing differences between left and right sided kernel estimates using smooth kernel functions. A local deviation process around the discontinuity location is introduced and an invariance principle is established. This result is applied to obtain asymptotic distributions of these estimators as well as corresponding estimators for the jump size. The methods and results are applicable to randomly censored data.
Sankhyā: The Indian Journal of Statistics, Series A (1961-2002) © 1996 Indian Statistical Institute