new.lambda: Calculating new penalty parameter lambda
In pendensity: Density Estimation with a Penalized Mixture Approach
Description
Usage
Arguments
Details
Value
Author(s)
References
Description
Calculating new penalty parameter lambda.
Usage
1
new.lambda(penden.env,lambda0)
Arguments
penden.env
Containing all information, environment of pendensity()
lambda0
actual penalty parameter lambda
Details
Iterating for the lambda is stopped, when the changes between the old and the new lambda is smaller than 0.01*lambda0. If this criterion isn't reached, the iteration is terminated after 11 iterations.
The iteration formulae is
\eqn{λ=(β^T D_m β)/(df(λ)-(m-1)}
Value
Returning the new lambda.
Author(s)
Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>
References
Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.
pendensity documentation built on May 2, 2019, 3:58 a.m.
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Description Usage Arguments Details Value Author(s) References
Description
Calculating new penalty parameter lambda.
Usage
1 | new.lambda(penden.env,lambda0)
|
Arguments
penden.env |
Containing all information, environment of pendensity() |
lambda0 |
actual penalty parameter lambda |
Details
Iterating for the lambda is stopped, when the changes between the old and the new lambda is smaller than 0.01*lambda0. If this criterion isn't reached, the iteration is terminated after 11 iterations.
The iteration formulae is
\eqn{λ=(β^T D_m β)/(df(λ)-(m-1)}
Value
Returning the new lambda.
Author(s)
Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>
References
Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.
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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