ltsbaseSummary: Summarizing the results of the best model
In ltsbase: Ridge and Liu Estimates based on LTS (Least Trimmed Squares) Method
Description
Usage
Arguments
Details
References
View source: R/ltsbaseSummary.R
Description
Returns and lists the minimum MSE value, the biasing parameter obtained at that minimum MSE value and extract the coefficients of the corresponding regression model given in object.
Usage
1
ltsbaseSummary(object)
Arguments
object
an object of class "ltsbase", usually, a result of a call to ltsbase.
Details
The model fitted includes no intercept term. ltsbaseSummary computes the modified MSE for Ridge and Liu estimates based on LTS method.
References
There are other MSE comparisons of the estimators such as:
F. Akdeniz and H. Erol (2003) Mean squared error matrix comparisons of some biased estimators in linear regression, Comm. Statist. Theory Methods, 32, 2389-2413.
ltsbase documentation built on May 2, 2019, 8:31 a.m.
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Description Usage Arguments Details References
View source: R/ltsbaseSummary.R
Description
Returns and lists the minimum MSE value, the biasing parameter obtained at that minimum MSE value and extract the coefficients of the corresponding regression model given in object.
Usage
1 | ltsbaseSummary(object)
|
Arguments
object |
an object of class " |
Details
The model fitted includes no intercept term. ltsbaseSummary computes the modified MSE for Ridge and Liu estimates based on LTS method.
References
There are other MSE comparisons of the estimators such as:
F. Akdeniz and H. Erol (2003) Mean squared error matrix comparisons of some biased estimators in linear regression, Comm. Statist. Theory Methods, 32, 2389-2413.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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