Some articles on estimators:
Redescending M-estimator
... In statistics, Redescending M-estimators are Ψ-type M-estimators which have ψ functions that are non-decreasing near the origin, but decreasing toward 0 far from the origin ... Due to these properties of the ψ function, these kinds of estimators are very efficient, have a high breakdown point and, unlike other outlier rejection techniques, they do not suffer from a masking effect ...
... In statistics, Redescending M-estimators are Ψ-type M-estimators which have ψ functions that are non-decreasing near the origin, but decreasing toward 0 far from the origin ... Due to these properties of the ψ function, these kinds of estimators are very efficient, have a high breakdown point and, unlike other outlier rejection techniques, they do not suffer from a masking effect ...
Construction Estimating Software - History - Spreadsheets
... Estimators used columnar sheets of paper to organize the take off and the estimate itself into rows of items and columns containing the description, quantity and the pricing components ... With the advent of computers in business, estimators began using spreadsheet applications like VisiCalc, Lotus 1-2-3, and Microsoft Excel to duplicate the traditional tabular format ... Many construction cost estimators (over 55%) continue to rely primarily upon manual methods, hard copy documents, and/or electronic spreadsheets such as ...
... Estimators used columnar sheets of paper to organize the take off and the estimate itself into rows of items and columns containing the description, quantity and the pricing components ... With the advent of computers in business, estimators began using spreadsheet applications like VisiCalc, Lotus 1-2-3, and Microsoft Excel to duplicate the traditional tabular format ... Many construction cost estimators (over 55%) continue to rely primarily upon manual methods, hard copy documents, and/or electronic spreadsheets such as ...
Sample Maximum And Minimum - Applications - Estimation
... outliers, the sample extrema cannot reliably be used as estimators unless data is clean – robust alternatives include the first and last deciles ... However, with clean data or in theoretical settings, they can sometimes prove very good estimators, particularly for platykurtic distributions, where for small data sets the mid-range is the most ... They are inefficient estimators of location for mesokurtic distributions, such as the normal distribution, and leptokurtic distributions, however ...
... outliers, the sample extrema cannot reliably be used as estimators unless data is clean – robust alternatives include the first and last deciles ... However, with clean data or in theoretical settings, they can sometimes prove very good estimators, particularly for platykurtic distributions, where for small data sets the mid-range is the most ... They are inefficient estimators of location for mesokurtic distributions, such as the normal distribution, and leptokurtic distributions, however ...
M-estimator
... In statistics, M-estimators are a broad class of estimators, which are obtained as the minima of sums of functions of the data ... Least-squares estimators and many maximum-likelihood estimators are M-estimators ... The definition of M-estimators was motivated by robust statistics, which contributed new types of M-estimators ...
... In statistics, M-estimators are a broad class of estimators, which are obtained as the minima of sums of functions of the data ... Least-squares estimators and many maximum-likelihood estimators are M-estimators ... The definition of M-estimators was motivated by robust statistics, which contributed new types of M-estimators ...
Maximum A Posteriori Estimation - Criticism
... While MAP estimation is a limit of Bayes estimators (under the 0-1 loss function), it is not very representative of Bayesian methods in general ... This is both because these estimators are optimal under squared-error and linear-error loss respectively - which are more representative of typical loss ... Finally, unlike ML estimators, the MAP estimate is not invariant under reparameterization ...
... While MAP estimation is a limit of Bayes estimators (under the 0-1 loss function), it is not very representative of Bayesian methods in general ... This is both because these estimators are optimal under squared-error and linear-error loss respectively - which are more representative of typical loss ... Finally, unlike ML estimators, the MAP estimate is not invariant under reparameterization ...
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