the Hodges–Lehmann estimator is a consistent and median-unbiased estimate of the population median. For non-symmetric populations, the Hodges–Lehmann estimator...

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

one of the eponyms of the Lehmann–Scheffé theorem and of the Hodges–Lehmann estimator of the median of a population. Lehmann was born in Strasbourg, Alsace-Lorraine...

rest of his career. Hodges is best known for his contributions to the field of statistics, including the Hodges–Lehmann estimator, the nearest neighbor...

statistic is the unique best unbiased estimator of that quantity. The Lehmann–Scheffé theorem is named after Erich Leo Lehmann and Henry Scheffé, given their...

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

delete-1, such as the delete-m jackknife or the delete-all-but-2 Hodges–Lehmann estimator, overcome this problem for the medians and quantiles by relaxing...

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

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...

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...

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

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

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...

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...

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...

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

The ratio estimator is a statistical estimator for the ratio of means of two random variables. Ratio estimates are biased and corrections must be made...

The Nelson–Aalen estimator is a non-parametric estimator of the cumulative hazard rate function in case of censored data or incomplete data. It is used...

History of randomness History of statistics Hitting time Hodges' estimator Hodges–Lehmann estimator Hoeffding's independence test Hoeffding's lemma Hoeffding's...

treatments) was quantified using the Hodges–Lehmann (HL) estimator, which is consistent with the Wilcoxon test. This estimator (HLΔ) is the median of all possible...

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...

estimator approaches the MAP estimator, provided that the distribution of θ {\displaystyle \theta } is quasi-concave. But generally a MAP estimator is...

{\displaystyle g_{2}} above is a biased estimator of the population excess kurtosis. An alternative estimator of the population excess kurtosis, which...

maximize (as in Fisher's exact test). Another alternative estimator is the Mantel–Haenszel estimator.[citation needed] The following four contingency tables...

\quad } therefore r is a biased estimator of ρ . {\displaystyle \rho .} The unique minimum variance unbiased estimator radj is given by where: r , n {\displaystyle...

Gauss–Markov theorem does not apply. Similar to the Hodges' estimator, the James-Stein estimator is superefficient and non-regular at θ = 0 {\displaystyle...

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

estimates. Unfortunately, when there are outliers in the data, classical estimators often have very poor performance, when judged using the breakdown point...

have a random sample of n people. The sample mean could serve as a good estimator of the population mean. Then we have: The difference between the height...

In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value...