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...

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...

estimation M-estimator: an approach used in robust statistics Maximum a posteriori (MAP) estimator: for a contrast in the way to calculate estimators when prior...

L-estimators are a general class of simple statistics, often robust, while M-estimators are a general class of robust statistics, and are now the preferred solution...

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

Two-step M-estimators deals with M-estimation problems that require preliminary estimation to obtain the parameter of interest. Two-step M-estimation...

James–Stein estimator is an estimator of the mean θ := ( θ 1 , θ 2 , … θ m ) {\displaystyle {\boldsymbol {\theta }}:=(\theta _{1},\theta _{2},\dots \theta _{m})}...

robust statistics, M-estimation and additive modelling. Winsorizing Robust regression M-estimator Visual comparison of different M-estimators Huber, Peter J...

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

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...

Bayesian L 1 {\displaystyle L_{1}} estimator: m ( X | Y = y ) = arg ⁡ min f E ⁡ [ | X − f ( Y ) | ] {\displaystyle m(X|Y=y)=\arg \min _{f}\operatorname...

In statistics, redescending M-estimators are Ψ-type M-estimators which have ψ functions that are non-decreasing near the origin, but decreasing toward...

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

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

In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter θ0—having the...

see that the MAP estimator for μ is given by μ ^ M A P = σ m 2 n σ m 2 n + σ v 2 ( 1 n ∑ j = 1 n x j ) + σ v 2 σ m 2 n + σ v 2 μ 0 = σ m 2 ( ∑ j = 1 n x...

In statistics, an L-estimator (or L-statistic) is an estimator which is a linear combination of order statistics of the measurements. This can be as little...

the bootstrap. Given a sample of size n {\displaystyle n} , a jackknife estimator can be built by aggregating the parameter estimates from each subsample...

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...

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...

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...

squares Line fitting Linear classifier Linear equation Logistic regression M-estimator Multivariate adaptive regression spline Nonlinear regression Nonparametric...

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

In statistical decision theory, a minimax estimator δ M {\displaystyle \delta ^{M}\,\!} is an estimator which performs best in the worst possible case...

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...

two-step M-estimator and thus consistency and asymptotic normality of the estimator can be verified using the general theory of two-step M-estimator. As in...

− m i ) 2 m i = ∑ i = 1 k x i 2 m i − n {\displaystyle X^{2}=\sum _{i=1}^{k}{\frac {(x_{i}-m_{i})^{2}}{m_{i}}}=\sum _{i=1}^{k}{{\frac {x_{i}^{2}}{m_{i}}}-n}}...

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...

is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with...

estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed...