NAG CL Interface
g02hfc (robustm_​user_​varmat)

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1 Purpose

g02hfc calculates an estimate of the asymptotic variance-covariance matrix for the bounded influence regression estimates (M-estimates). It is intended for use with g02hdc.

2 Specification

#include <nag.h>
void  g02hfc (Nag_OrderType order,
double (*psi)(double t, Nag_Comm *comm),
double (*psp)(double t, Nag_Comm *comm),
Nag_RegType regtype, Nag_CovMatrixEst covmat_est, double sigma, Integer n, Integer m, const double x[], Integer pdx, const double rs[], const double wgt[], double cov[], Integer pdc, double comm_arr[], Nag_Comm *comm, NagError *fail)
The function may be called by the names: g02hfc, nag_correg_robustm_user_varmat or nag_robust_m_regsn_param_var.

3 Description

For a description of bounded influence regression see g02hdc. Let θ be the regression parameters and let C be the asymptotic variance-covariance matrix of θ^. Then for Huber type regression
C=fH(XTX)−1σ^2,  
where
fH=1n-m i= 1nψ2 (ri/σ^) (1nψ(riσ^)) 2 κ2  
κ2=1+mn 1n i=1n (ψ(ri/σ^)-1ni=1nψ(ri/σ^)) 2 (1ni=1nψ(riσ^)) 2 ,  
see Huber (1981) and Marazzi (1987).
For Mallows and Schweppe type regressions, C is of the form
σ^n2S1−1S2S1−1,  
where S1=1nXTDX and S2=1nXTPX.
D is a diagonal matrix such that the ith element approximates E(ψ(ri/(σwi))) in the Schweppe case and E(ψ(ri/σ)wi) in the Mallows case.
P is a diagonal matrix such that the ith element approximates E(ψ2(ri/(σwi))wi2) in the Schweppe case and E(ψ2(ri/σ)wi2) in the Mallows case.
Two approximations are available in g02hfc:
  1. 1.Average over the ri
    Schweppe Mallows Di=(1nj=1nψ(rjσ^wi )) wi Di=(1nj=1nψ(rjσ^)) wi Pi=(1nj=1nψ2(rjσ^wi )) wi2 Pi=(1nj=1nψ2(rjσ^)) wi2  
  2. 2.Replace expected value by observed
    Schweppe Mallows Di=ψ ( riσ ^wi ) wi Di=ψ ( riσ ^) wi Pi=ψ2 ( riσ ^wi ) wi2 Pi=ψ2 ( riσ ^) wi2  
See Hampel et al. (1986) and Marazzi (1987).
In all cases σ^ is a robust estimate of σ.
g02hfc is based on routines in ROBETH; see Marazzi (1987).

4 References

Hampel F R, Ronchetti E M, Rousseeuw P J and Stahel W A (1986) Robust Statistics. The Approach Based on Influence Functions Wiley
Huber P J (1981) Robust Statistics Wiley
Marazzi A (1987) Subroutines for robust and bounded influence regression in ROBETH Cah. Rech. Doc. IUMSP, No. 3 ROB 2 Institut Universitaire de Médecine Sociale et Préventive, Lausanne

5 Arguments

1: order Nag_OrderType Input
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2: psi function, supplied by the user External Function
psi must return the value of the ψ function for a given value of its argument.
The specification of psi is:
double  psi (double t, Nag_Comm *comm)
1: t double Input
On entry: the argument for which psi must be evaluated.
2: comm Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to psi.
userdouble *
iuserInteger *
pPointer 
The type Pointer will be void *. Before calling g02hfc you may allocate memory and initialize these pointers with various quantities for use by psi when called from g02hfc (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).
Note: psi should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by g02hfc. If your code inadvertently does return any NaNs or infinities, g02hfc is likely to produce unexpected results.
3: psp function, supplied by the user External Function
psp must return the value of ψ(t)=ddt ψ(t) for a given value of its argument.
The specification of psp is:
double  psp (double t, Nag_Comm *comm)
1: t double Input
On entry: the argument for which psp must be evaluated.
2: comm Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to psp.
userdouble *
iuserInteger *
pPointer 
The type Pointer will be void *. Before calling g02hfc you may allocate memory and initialize these pointers with various quantities for use by psp when called from g02hfc (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).
Note: psp should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by g02hfc. If your code inadvertently does return any NaNs or infinities, g02hfc is likely to produce unexpected results.
4: regtype Nag_RegType Input
On entry: the type of regression for which the asymptotic variance-covariance matrix is to be calculated.
regtype=Nag_MallowsReg
Mallows type regression.
regtype=Nag_HuberReg
Huber type regression.
regtype=Nag_SchweppeReg
Schweppe type regression.
Constraint: regtype=Nag_MallowsReg, Nag_HuberReg or Nag_SchweppeReg.
5: covmat_est Nag_CovMatrixEst Input
On entry: if regtypeNag_HuberReg, covmat_est must specify the approximation to be used.
If covmat_est=Nag_CovMatAve, averaging over residuals.
If covmat_est=Nag_CovMatObs, replacing expected by observed.
If regtype=Nag_HuberReg, covmat_est is not referenced.
Constraint: covmat_est=Nag_CovMatAve or Nag_CovMatObs.
6: sigma double Input
On entry: the value of σ^, as given by g02hdc.
Constraint: sigma>0.0.
7: n Integer Input
On entry: n, the number of observations.
Constraint: n>1.
8: m Integer Input
On entry: m, the number of independent variables.
Constraint: 1m<n.
9: x[dim] const double Input
Note: the dimension, dim, of the array x must be at least
  • max(1,pdx×m) when order=Nag_ColMajor;
  • max(1,n×pdx) when order=Nag_RowMajor.
where X(i,j) appears in this document, it refers to the array element
  • x[(j-1)×pdx+i-1] when order=Nag_ColMajor;
  • x[(i-1)×pdx+j-1] when order=Nag_RowMajor.
On entry: the values of the X matrix, i.e., the independent variables. X(i,j) must contain the ijth element of X, for i=1,2,,n and j=1,2,,m.
10: pdx Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array x.
Constraints:
  • if order=Nag_ColMajor, pdxn;
  • if order=Nag_RowMajor, pdxm.
11: rs[n] const double Input
On entry: the residuals from the bounded influence regression. These are given by g02hdc.
12: wgt[n] const double Input
On entry: if regtypeNag_HuberReg, wgt must contain the vector of weights used by the bounded influence regression. These should be used with g02hdc.
If regtype=Nag_HuberReg, wgt is not referenced.
13: cov[dim] double Output
Note: the dimension, dim, of the array cov must be at least pdc×m.
the (i,j)th element of the matrix is stored in
  • cov[(j-1)×pdc+i-1] when order=Nag_ColMajor;
  • cov[(i-1)×pdc+j-1] when order=Nag_RowMajor.
On exit: the estimate of the variance-covariance matrix.
14: pdc Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array cov.
Constraint: pdcm.
15: comm_arr[dim] double Output
Note: the dimension, dim, of the array comm_arr must be at least m×(n+m+1)+2×n.
On exit: if regtypeNag_HuberReg, comm_arr[i-1], for i=1,2,,n, will contain the diagonal elements of the matrix D and comm_arr[i-1], for i=n+1,,2n, will contain the diagonal elements of matrix P.
16: comm Nag_Comm *
The NAG communication argument (see Section 3.1.1 in the Introduction to the NAG Library CL Interface).
17: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_CORRECTION_FACTOR
Either the value of 1ni=1nψ ( riσ^)=0,
or κ=0,
or i=1nψ2 ( riσ^)=0.
In this situation g02hfc returns C as (XTX)−1.
NE_INT
On entry, m=value.
Constraint: m1.
On entry, n=value.
Constraint: n>1.
On entry, pdc=value.
Constraint: pdc>0.
On entry, pdx=value.
Constraint: pdx>0.
NE_INT_2
On entry, m=value and n=value.
Constraint: 1m<n.
On entry, m=value and pdc=value.
Constraint: pdcm.
On entry, n=value and m=value.
Constraint: n>m.
On entry, pdc=value and m=value.
Constraint: pdcm.
On entry, pdx=value and m=value.
Constraint: pdxm.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_POS_DEF
XTX matrix not positive definite.
NE_REAL
On entry, sigma=value.
Constraint: sigma0.0.
NE_SINGULAR
S1 matrix is singular or almost singular.

7 Accuracy

In general, the accuracy of the variance-covariance matrix will depend primarily on the accuracy of the results from g02hdc.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
g02hfc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g02hfc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

g02hfc is only for situations in which X has full column rank.
Care has to be taken in the choice of the ψ function since if ψ(t)=0 for too wide a range then either the value of fH will not exist or too many values of Di will be zero and it will not be possible to calculate C.

10 Example

The asymptotic variance-covariance matrix is calculated for a Schweppe type regression. The values of X, σ^ and the residuals and weights are read in. The averaging over residuals approximation is used.

10.1 Program Text

Program Text (g02hfce.c)

10.2 Program Data

Program Data (g02hfce.d)

10.3 Program Results

Program Results (g02hfce.r)