Anagram & Information om | Engelska ordet ESTIMATORS

Exempel på hur man kan använda ESTIMATORS i en mening

• A related concept is that of linear sufficiency, which is weaker than sufficiency but can be applied in some cases where there is no sufficient statistic, although it is restricted to linear estimators.
• In statistics, the Gauss–Markov theorem (or simply Gauss theorem for some authors) states that the ordinary least squares (OLS) estimator has the lowest sampling variance within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances and expectation value of zero.
• As with other trimmed estimators, the main advantage of the trimmed mean is robustness and higher efficiency for mixed distributions and heavy-tailed distribution (like the Cauchy distribution), at the cost of lower efficiency for some other less heavily tailed distributions (such as the normal distribution).
• After the war, he worked on the theory and practice of time series analysis, and conclusively demonstrated (with the meager computing resources available at the time) that unsmoothed sample periodograms were unreliable estimators for the population spectrum.
• Traditionally, statisticians have evaluated estimators and designs by considering some summary statistic of the covariance matrix (of an unbiased estimator), usually with positive real values (like the determinant or matrix trace).
• Phrased otherwise, unbiasedness is not a requirement for consistency, so biased estimators and tests may be used in practice with the expectation that the outcomes are reliable, especially when the sample size is large (recall the definition of consistency).
• Ridge regression was developed as a possible solution to the imprecision of least square estimators when linear regression models have some multicollinear (highly correlated) independent variables—by creating a ridge regression estimator (RR).
• Also, these estimators are weakly consistent and plugging them into the SE estimator makes it also weakly consistent.
• It is known that the least squares estimator minimizes the variance of mean-unbiased estimators (under the conditions of the Gauss–Markov theorem).
• Slutsky's theorem can be used to combine several different estimators, or an estimator with a non-random convergent sequence.
• The direction of the bias depends on the estimators as well as the covariance between the regressors and the omitted variables.
• In some cases biased estimators have lower MSE because they have a smaller variance than does any unbiased estimator; see estimator bias.
• The OLS estimator is consistent for the level-one fixed effects when the regressors are exogenous and forms perfect colinearity (rank condition), consistent for the variance estimate of the residuals when regressors have finite fourth moments and—by the Gauss–Markov theorem—optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated.
• The latter assumes an a priori distribution of examinee ability, and has two commonly used estimators: expectation a posteriori and maximum a posteriori.
• For random vectors, since the MSE for estimation of a random vector is the sum of the MSEs of the coordinates, finding the MMSE estimator of a random vector decomposes into finding the MMSE estimators of the coordinates of X separately:.
• Winsorized estimators are usually more robust to outliers than their more standard forms, although there are alternatives, such as trimming (see below), that will achieve a similar effect.
• In the case of extremum estimators for parametric models, a certain objective function is maximized or minimized over the parameter space.
• by replacing estimators that are optimal under the assumption of a normal distribution with estimators that are optimal for, or at least derived for, other distributions; for example, using the t-distribution with low degrees of freedom (high kurtosis) or with a mixture of two or more distributions.
• Pseudocounts (or Laplace estimators) are often applied when calculating PPMs if based on a small dataset, in order to avoid matrix entries having a value of 0.
• With suitable rescaling, M-estimators are special cases of extremum estimators (in which more general functions of the observations can be used).

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