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In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed...

Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental...

In statistics and in particular statistical theory, unbiased estimation of a standard deviation is the calculation from a statistical sample of an estimated...

performance analysis tools. Due to these disadvantages, gate-level power estimation tools have begun to gain some acceptance where faster, probabilistic techniques...

Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how...

and other forms of penalized estimation, such as Lasso regression, deliberately introduce bias into the estimation of β in order to reduce the variability...

published in 1934, I recognized Fisher's priority for the idea that interval estimation is possible without any reference to Bayes' theorem and with the solution...

In mathematics the estimation lemma, also known as the ML inequality, gives an upper bound for a contour integral. If f is a complex-valued, continuous...

channel estimation scenario, as the goal is to minimize the total channel estimation error. The James–Stein estimator has also found use in fundamental...

of the dependent variable, y i {\displaystyle y_{i}} . One method of estimation is ordinary least squares. This method obtains parameter estimates that...

important to be able to accurately estimate premorbid intelligence. Accurate estimation allows the quantification of the impacts of neurological damage or decline...

In computer science, the count-distinct problem (also known in applied mathematics as the cardinality estimation problem) is the problem of finding the...

Estimation of signal parameters via rotational invariant techniques (ESPRIT), is a technique to determine the parameters of a mixture of sinusoids in...

In statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated. Estimation of covariance matrices...

methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation...

In statistics, adaptive or "variable-bandwidth" kernel density estimation is a form of kernel density estimation in which the size of the kernels used...

histogram-based estimation, but other approaches have been developed and used, each with its own benefits and drawbacks. The main factor in choosing a method...

non-linearly dependent yet estimation may still be consistent, we might suspect the response depends linearly both on a value and its square; in which case we would...

approach. The third approach is a nested estimation whereby the local estimation for individual series is programmed in as a part of the model definition. This...

observer structures used for state estimation of linear and nonlinear systems. A linear observer structure is described in the following sections. The state...