N.I.M.R.O.D.

# penalization.f90 File Reference

Go to the source code of this file.

## Functions/Subroutines

subroutine logLikelihood_penalization (bparam, pena)
Penalization for log-likelihood.
Penalization for gradients used in scores.
subroutine hessian_penalization (bparam, pena)
Penalization for diagonal of the hessian terms.

## Function Documentation

 subroutine gradients_penalization ( double precision,dimension(npm),intent(in) bparam, double precision,dimension(npm),intent(out) pena )

Penalization for gradients used in scores.

AUTHOR : Melanie Prague Daniel Commenges Julia Drylewicz Jeremy guedj Rodolphe Thiebaut

DESCRIPTION :

We assume normal independent priors for the fixed effects, half-Cauchy priors for the variances of the random effects with median parameter and conventional one-dimension Jeffreys-type improper priors for the variances of the measurement errors, then the penalization function where is:

where and are the expectation and the variance under the prior.This must correspond to the first derivative of penalization defined in logLikelihood_penalization.

MODIFICATION:

01/09/2012 - Prague - Refactoring

INFORMATIONS:

Parameters:
 [in] bparam parameter vector [out] pena Penalization value

Definition at line 114 of file penalization.f90.

Referenced by derivMARC(), and derivRVS().

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 subroutine hessian_penalization ( double precision,dimension(npm),intent(in) bparam, double precision,dimension(npm),intent(out) pena )

Penalization for diagonal of the hessian terms.

AUTHOR : Melanie Prague Daniel Commenges Julia Drylewicz Jeremy guedj Rodolphe Thiebaut

DESCRIPTION :

We assume normal independent priors for the fixed effects, half-Cauchy priors for the variances of the random effects with median parameter and conventional one-dimension Jeffreys-type improper priors for the variances of the measurement errors, then the penalization function where is:

where and are the expectation and the variance under the prior. This must correspond to the second derivative of penalization defined in logLikelihood_penalization.

MODIFICATION:

01/09/2012 - Prague - Refactoring

INFORMATIONS:

Parameters:
 [in] bparam parameter vector [out] pena Penalization value

Definition at line 190 of file penalization.f90.

Referenced by derivMARC(), and derivRVS().

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 subroutine logLikelihood_penalization ( double precision,dimension(npm),intent(in) bparam, double precision,intent(out) pena )

Penalization for log-likelihood.

AUTHOR : Melanie Prague Daniel Commenges Julia Drylewicz Jeremy guedj Rodolphe Thiebaut

DESCRIPTION :

We assume normal independent priors for the fixed effects, half-Cauchy priors for the variances of the random effects with median parameter and conventional one-dimension Jeffreys-type improper priors for the variances of the measurement errors, then the penalization function can be written:

where and are the expectation and the variance under the prior.

CAUTION : Normally, these subroutines must not be modified in routine by users.

MODIFICATION:

01/09/2012 - Prague - Refactoring

INFORMATIONS:

Parameters:
 [in] bparam parameter vector [out] pena Penalization value

Definition at line 39 of file penalization.f90.

Referenced by funcpa().

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