NDLI: Survival analysis in Johnson

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Strong consistency of Kernel density estimates for Markov chains failure rates

Penalized maximum likelihood estimation for a function of the intensity of a Poisson point process

Sequential change-point detection for mixing random sequences under composite hypotheses

Survival analysis in Johnson–Mehl Tessellation

Strong convergence rates for the estimation of a covariance operator for associated samples

Consistent estimation of covariation under nonsynchronicity

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Survival analysis in Johnson–Mehl Tessellation

Content Provider	Springer Nature Link
Author	Saada, Diane Aletti, Giacomo
Copyright Year	2007
Abstract	The crystallization process is represented here by a generalized Boolean model, whose parameters are usually unknown. A better understanding of the model may be obtained if we estimate the corresponding parameters. In this paper, we provide non-parametric estimators for the parameters of the model. Among them, the degree of crystallinity at time t is the probability that an arbitrary point in the space has been captured by a crystal before time t. We estimate it following the Kaplan–Meier approach extended to the context of a Johnson–Mehl incomplete tessellation. Three estimators are defined, according to the kind of data we dispose. The results are also illustrated by simulations. We also provide estimators for the parameters describing geometrical aspects of the phenomenon.
Ending Page	76
Page Count	22
Starting Page	55
File Format	PDF
ISSN	13870874
e-ISSN	15729311
Journal	Statistical Inference for Stochastic Processes
Issue Number	1
Volume Number	11
Language	English
Publisher	Springer Netherlands
Publisher Date	2007-03-09
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Point processes Nelson–Aalen estimator Estimation Crystallization process Johnson–Mehl tessellations Kaplan–Meier estimator Applications in engineering and industry Probability Theory and Stochastic Processes
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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