This paper integrates expert knowledge into the product-limit estimator in two different ways with distinct interpretations. Strong uniform consistency is proved for both cases under certain assumptions on the kind of contamination and the quality of expert information, which sheds light on the ...
Kaplan-Meier估计器与约束的右 цensor数据--递归计算算法 V0.4-2 说明书 Package‘kmc’November22,2022 Type Package Title Kaplan-Meier Estimator with Constraints for Right Censored Data --a Recursive Computational Algorithm Version0.4-2 Date2022-11-21 Maintainer Yifan Yang<***> Description Given c...
What is the product-limit estimator? It is a name for the Kaplan-Meier estimator. Proper planning in your business Being able to estimate when an event may occur is important for the planning of your business. Things happen, but with tools like the Kaplan-Meier estimator, you can be better...
The estimator is based upon the entire range of data. Note that some software uses only the data up to the last observed event; Hosmer and Lemeshow (1999) point out that this biases the estimate of the mean downwards, and they recommend that the entire range of data is used. A large ...
By multiplying the weight to each patient, Kaplan-Meier curves can be created for the SCE to outcomes with censoring. The HR is then calculated using a weighted proportional hazard model. For this method, two assumptions need to be introduced to achieve unbiasedness.Results The proposed method ...
Product-limit estimatorKaplan-Meier estimatorrandom censorshipsurvival dataconfidence bandsmean life-timecounting processesmartingalesstochastic integralsweak convergence... Gill,Richard - 《Annals of Statistics》 被引量: 601发表: 1983年 Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes...
Stationary processFunctional central limit theoremLimit distributionNelson–Aalen estimatorKaplan–Meier estimatorWe derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions...
Kaplan–Meier (KM) estimator proposed by [21] has been a popular method in time-to-event data (see, e.g., [22,23,24]), as it is a nonparametric approach without stringent model assumptions and describes the survival probabilities directly. KM estimator has also been used in functional da...
Kaplan–Meier (KM) estimator proposed by [21] has been a popular method in time-to-event data (see, e.g., [22,23,24]), as it is a nonparametric approach without stringent model assumptions and describes the survival probabilities directly. KM estimator has also been used in functional da...