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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 goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also known as the power spectral density)...

a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior...

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...

uniform in the region of interest. In frequentist inference, MLE is a special case of an extremum estimator, with the objective function being the likelihood...

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

but includes vector valued or function valued estimators. Estimation theory is concerned with the properties of estimators; that is, with defining properties...

median is the estimator that minimizes expected loss experienced under the absolute-difference loss function. Still different estimators would be optimal...

the medians of the subsamples. Any mean-unbiased estimator minimizes the risk (expected loss) with respect to the squared-error loss function, as observed...

outliers in the data, classical estimators often have very poor performance, when judged using the breakdown point and the influence function described...

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 being...

their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements. In estimation...

arbitrarily crude estimator into an estimator that is optimal by the mean-squared-error criterion or any of a variety of similar criteria. The Rao–Blackwell...

In that sense, the maximum likelihood estimator is implicitly defined by the value at 0 {\textstyle \mathbf {0} } of the inverse function s n − 1 : E d...

mean-unbiased estimator for θ. In other words, this statistic has a smaller expected loss for any convex loss function; in many practical applications with the squared...

distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical...

estimator – redirects to Maximum a posteriori estimation Marchenko–Pastur distribution Marcinkiewicz–Zygmund inequality Marcum Q-function Margin of error...

of typical loss functions—and for a continuous posterior distribution there is no loss function which suggests the MAP is the optimal point estimator...

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

estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust...