Results 211 to 220 of about 17,216 (258)

Kaplan–Meier Estimator Under Association

Journal of Multivariate Analysis, 1998
This work studies the Kaplan-Meier estimator and the estimation of the hazard function in a model with censored failure times. The true survival times \(T_1,\dots T_n\), with common marginal \(F\), are not assumed mutually independent. They satisfy two different notions of weak dependence: a) They are positively associated, i.e.
Zongwu Cai, George G Roussas
exaly   +3 more sources

A Combinatoric Approach to the Kaplan-Meier Estimator

Annals of Statistics, 1985
The paper considers the Kaplan-Meier estimator \(F_ n^{KM}\) from a combinatoric viewpoint. Under the assumption that the estimated distribution F and the censoring distribution G are continuous, the combinatoric results are used to show that \(\int | \theta (z)| dF_ n^{KM}(z)\) has expectation not larger than \(\int | \theta (z)| dF(z)\) for any ...
exaly   +4 more sources

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Expert Kaplan–Meier estimation

Scandinavian Actuarial Journal, 2023
The setting of a right-censored random sample subject to contamination is considered. In various fields, expert information is often available and used to overcome the contamination. This paper integrates expert knowledge into the product-limit estimator in two different ways with distinct interpretations.
Martin Bladt, Christian Furrer
openaire   +2 more sources

The Jackknife Estimate of a Kaplan-Meier Integral

Biometrika, 1994
Summary: We derive an explicit formula for the jackknife estimate of a Kaplan- Meier integral. From this the asymptotic analysis of the jackknifed Kaplan-Meier process becomes straightforward. In a small simulation study it is demonstrated that jackknifing may lead to a considerable reduction of the bias.
Stute, Winfried, Wang, Jane-Ling
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A Note on the Uniform Consistency of the Kaplan-Meier Estimator

Annals of Statistics, 1987
Let \(\{X_ n\), \(n\geq 1\}\) be i.i.d. with \(P(X_ i\leq u)=F(u)\) and \(\{U_ n\), \(n\geq 1\}\) be i.i.d. with \(P(U_ i\leq u)=G(u)\). \(\hat F_ n(t)\) is the Kaplan-Meier estimator based on the censored data \((\tilde X_ i=X_ i\wedge U_ i\), \(\delta_ i=1_{(X_ i\leq U_ i)}\), \(1\leq i\leq n)\).
exaly   +3 more sources

On an exponential bound for the Kaplan–Meier estimator

Lifetime Data Analysis, 2007
We review limit theory and inequalities for the Kaplan-Meier Kaplan and Meier (J Am Stat Assoc 53:457-481, 1958) product limit estimator of a survival function on the whole line [Formula: see text] . Along the way we provide bounds for the constant in an interesting inequality due to Biotouzé et al.
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Reconstructing the Kaplan–Meier Estimator as an M-estimator

The American Statistician, 2021
The Kaplan–Meier (KM) estimator, which provides a nonparametric estimate of a survival function for time-to-event data, has broad applications in clinical studies, engineering, economics and many o...
Jiaqi Gu, Yiwei Fan, Guosheng Yin
openaire   +1 more source

Extensions of the kaplan-meier estimator

Communications in Statistics - Simulation and Computation, 1995
The Kaplan–Meier estimation (KME) (1958) is a popular nonparametric method in analyzing the survival data. Efron (1967) proposes a re-distribution-to-the-right algorithm for right censored data, which can also be re-distributed from right to left by a method of Dinse (1985).
Wei-Ting Kary Chien, Way Kuo
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Bootstrapping the Kaplan-Meier Estimator

Journal of the American Statistical Association, 1986
Abstract Randomly censored data consist of iid pairs of observations (Xi, δi), i = 1, …, n; if δ i = 0, Xi denotes a censored observation, and if δ i = 1, Xi denotes an exact “survival” time, which is the variable of interest. For estimating the distribution F of the survival times, the product-limit estimator proposed by Kaplan and Meier (1958) has ...
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A Note on the Kaplan—Meier Estimator

The American Statistician, 1993
Abstract An anomalous feature of the Kaplan-Meier estimator is that certain estimated survival probabilities can be decreased when the data are perturbed in a way that improves the overall group survival. An alternative estimator based on the so-called reduced-sample method does not have this disadvantage.
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