Noun:

মূল্নির্ধারক,

estimator's Usage Examples:

function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated.

An estimator or decision.

function can be solved explicitly; for instance, the ordinary least squares estimator maximizes the likelihood of the linear regression model.

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

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

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

There are a variety of point estimators, each with different properties.

minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected.

statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average.

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

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

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.

which 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, M-estimators are a broad class of extremum estimators for which the objective function is a sample average.

statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity.

OLS estimator is consistent when the regressors are exogenous, and—by the Gauss–Markov theorem—optimal in the class of linear unbiased estimators when.

of quality of an estimator, of an experimental design, or of a hypothesis testing procedure.

Essentially, a more efficient estimator, experiment, or test.

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

But generally a MAP estimator is.

distribution on [0, b] with unknown b, the minimum-variance unbiased estimator (UMVUE) for the maximum is given by b ^ UMVU = k + 1 k m = m + m k {\displaystyle.

ordinary least squares (OLS) estimator has the lowest sampling variance within the class of linear unbiased estimators, if the errors in the linear regression.

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

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

Synonyms:

reckoner; statistician; adder; expert; computer; subtracter; calculator; actuary; figurer; number cruncher;

Antonyms:

unskilled; generalist; software;