The Kaplan–Meier estimator, also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime...

The Dvoretzky–Kiefer–Wolfowitz inequality is obtained for the Kaplan–Meier estimator which is a right-censored data analog of the empirical distribution...

Nelson-Aalen estimator is directly related to the Kaplan-Meier estimator and both maximize the empirical likelihood. "Kaplan–Meier and Nelson–Aalen Estimators"....

In statistics, the bias of an estimator (or bias function) is the difference between this estimator's expected value and the true value of the parameter...

Lynn Kaplan (May 11, 1920 – September 26, 2006) was a mathematician most famous for the Kaplan–Meier estimator, developed together with Paul Meier. Edward...

next observation time. The Kaplan–Meier estimator can be used to estimate the survival function. The Nelson–Aalen estimator can be used to provide a non-parametric...

Hodges–Lehmann estimator is a robust and highly efficient estimator of the population median; for non-symmetric distributions, the Hodges–Lehmann estimator is a...

method to model the survival function is the non-parametric Kaplan–Meier estimator. This estimator requires lifetime data. Periodic case (cohort) and death...

censoring is more likely in one group than another. Mathematics portal Kaplan–Meier estimator Hazard ratio Mantel, Nathan (1966). "Evaluation of survival data...

standard deviation. Such a statistic is called an estimator, and the estimator (or the value of the estimator, namely the estimate) is called a sample standard...

quantiles from a sample Frequency (statistics) Empirical likelihood Kaplan–Meier estimator for censored processes Survival function Q–Q plot A modern introduction...

that characterizes the transformation of an arbitrarily crude estimator into an estimator that is optimal by the mean-squared-error criterion or any of...

minimum-variance unbiased estimator (MVUE) or uniformly minimum-variance unbiased estimator (UMVUE) is an unbiased estimator that has lower variance than...

X_{2})} is sufficient but not complete. It admits a non-zero unbiased estimator of zero, namely X 1 − X 2 {\textstyle X_{1}-X_{2}} . Most parametric models...

can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when the random...

unbiased estimator (dividing by a number larger than n − 1) and is a simple example of a shrinkage estimator: one "shrinks" the unbiased estimator towards...

The standard error (SE) of a statistic (usually an estimator of a parameter, like the average or mean) is the standard deviation of its sampling distribution...

In statistics, M-estimators are a broad class of extremum estimators for which the objective function is a sample average. Both non-linear least squares...

K-means++ K-medians clustering K-medoids K-statistic Kalman filter Kaplan–Meier estimator Kappa coefficient Kappa statistic Karhunen–Loève theorem Kendall...

top-level domain (ccTLD) for Comoros Km, an electric motor constant Kaplan–Meier estimator, a non-parametric statistic used to estimate the survival function...

calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function...

of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs...

medicine. Meier is known for introducing, with Edward L. Kaplan, the Kaplan–Meier estimator, a method for measuring how many patients survive a medical...

their parameters and because the statistical properties of the resulting estimators are easier to determine. Linear regression has many practical uses. Most...

small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better...

of quality of an estimator, of an experimental design, or of a hypothesis testing procedure. Essentially, a more efficient estimator needs fewer input...

In statistics, the Hodges–Lehmann estimator is a robust and nonparametric estimator of a population's location parameter. For populations that are symmetric...

deviation ellipse is lower. The derivation of the maximum-likelihood estimator of the covariance matrix of a multivariate normal distribution is straightforward...

maximum likelihood estimator. s n ( θ ) = 0 {\displaystyle s_{n}(\theta )=\mathbf {0} } In that sense, the maximum likelihood estimator is implicitly defined...

modelling errors all have the same variance. While the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient...