Journal Article10.1080/10485259908832758
A new bandwidth selector in hazard estimation
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TL;DR: In this article, a new bandwidth selector was proposed for density estimation in the context of multivariate hazard rate estimation with right-censored data, and the relative rate of convergence of this new selector to the theoretical bandwidth that minimizes the MISE in square root n was proved.
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Abstract: This paper presents a new bandwidth selector (closely related to that proposed by Jones, Marron and Park, 1991, for density estimation) in the context of multivariate hazard rate estimation with right-censored data. We prove that the relative rate of convergence of this new selector to the theoretical bandwidth that minimizes the MISE in square root n. Some simulations are performed to compare the method with the cross-validation and the smoothed bootstrap selectors (in the one-dimensional case).
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Citations
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
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Estimating the Expectation of the Log-Likelihood with Censored Data for Estimator Selection
Benoit Liquet,Daniel Commenges +1 more
TL;DR: It is shown that likelihood cross-validation (LCV) is an estimator of ELL and three bootstrap estimators are exhibited, and the ELLbboot criterion is applied to compare the kernel and penalized likelihood estimators to estimate the risk of developing dementia for women using data from a large cohort study.
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Lower bounds on bandwidth selection in hazard estimation
TL;DR: In this article, lower bounds for bandwidth selection under right random censorship by the kernel method are provided for nonparametric hazard estimation under the assumption that the distribution function of the underlying lifetime is sufficiently regular.
Selecting a semi-parametric estimator by the expected log-likelihood
Benoit Liquet,Daniel Commenges +1 more
- 01 Jan 2006
TL;DR: In this paper, a criterion for choosing an estimator in a family of semi-parametric estimators from incomplete data is proposed, which is the expected observed log-likelihood (ELL).
References
•Book
Convergence of Probability Measures
Patrick Billingsley
- 01 Jan 1968
TL;DR: Weak Convergence in Metric Spaces as discussed by the authors is one of the most common modes of convergence in metric spaces, and it can be seen as a form of weak convergence in metric space.
15K
Convergence of Probability Measures
TL;DR: Convergence of Probability Measures as mentioned in this paper is a well-known convergence of probability measures. But it does not consider the relationship between probability measures and the probability distribution of probabilities.
5.9K
•Book
Martingale Limit Theory and Its Application
Peter Hall,E Lukacs,Z W Birnbaum,C. C. Heyde +3 more
- 23 Sep 2014
4K
A Brief Survey of Bandwidth Selection for Density Estimation
TL;DR: In this article, the authors recommend a "solve-the-equation" plug-in bandwidth selector as being most reliable in terms of overall performance for kernel density estimation.
1.4K