nag_robust_m_estim_1var_usr (g07dcc) (PDF version)
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NAG Library Manual

# NAG Library Function Documentnag_robust_m_estim_1var_usr (g07dcc)

## 1  Purpose

nag_robust_m_estim_1var_usr (g07dcc) computes an $M$-estimate of location with (optional) simultaneous estimation of scale, where you provide the weight functions.

## 2  Specification

 #include #include
void  nag_robust_m_estim_1var_usr (
 double (*chi)(double t, Nag_Comm *comm),
 double (*psi)(double t, Nag_Comm *comm),
Integer isigma, Integer n, const double x[], double beta, double *theta, double *sigma, Integer maxit, double tol, double rs[], Integer *nit, Nag_Comm *comm, NagError *fail)

## 3  Description

The data consists of a sample of size $n$, denoted by ${x}_{1},{x}_{2},\dots ,{x}_{n}$, drawn from a random variable $X$.
The ${x}_{i}$ are assumed to be independent with an unknown distribution function of the form,
 $Fxi-θ/σ$
where $\theta$ is a location argument, and $\sigma$ is a scale argument. $M$-estimators of $\theta$ and $\sigma$ are given by the solution to the following system of equations;
 $∑i=1nψxi-θ^/σ^ = 0 ∑i=1nχxi-θ^/σ^ = n-1β$
where $\psi$ and $\chi$ are user-supplied weight functions, and $\beta$ is a constant. Optionally the second equation can be omitted and the first equation is solved for $\stackrel{^}{\theta }$ using an assigned value of $\sigma ={\sigma }_{c}$.
The constant $\beta$ should be chosen so that $\stackrel{^}{\sigma }$ is an unbiased estimator when ${x}_{\mathit{i}}$, for $\mathit{i}=1,2,\dots ,n$ has a Normal distribution. To achieve this the value of $\beta$ is calculated as:
 $β=Eχ=∫-∞∞χz12πexp-z22dz$
The values of $\psi \left(\frac{{x}_{i}-\stackrel{^}{\theta }}{\stackrel{^}{\sigma }}\right)\stackrel{^}{\sigma }$ are known as the Winsorized residuals.
The equations are solved by a simple iterative procedure, suggested by Huber:
 $σ^k=1βn-1 ∑i=1nχ xi-θ^k-1σ^k-1 σ^k-12$
and
 $θ^k=θ^k- 1+1n ∑i= 1nψ xi-θ^k- 1σ^k σ^k$
or
 $σ^k=σc$
if $\sigma$ is fixed.
The initial values for $\stackrel{^}{\theta }$ and $\stackrel{^}{\sigma }$ may be user-supplied or calculated within nag_robust_m_estim_1var (g07dbc) as the sample median and an estimate of $\sigma$ based on the median absolute deviation respectively.
nag_robust_m_estim_1var_usr (g07dcc) is based upon function LYHALG within the ROBETH library, see Marazzi (1987).
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 estimation of location and scale in ROBETH Cah. Rech. Doc. IUMSP, No. 3 ROB 1 Institut Universitaire de Médecine Sociale et Préventive, Lausanne

## 5  Arguments

1:     chifunction, supplied by the userExternal Function
chi must return the value of the weight function $\chi$ for a given value of its argument. The value of $\chi$ must be non-negative.
The specification of chi is:
 double chi (double t, Nag_Comm *comm)
1:     tdoubleInput
On entry: the argument for which chi must be evaluated.
2:     commNag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to chi.
userdouble *
iuserInteger *
pPointer
The type Pointer will be void *. Before calling nag_robust_m_estim_1var_usr (g07dcc) you may allocate memory and initialize these pointers with various quantities for use by chi when called from nag_robust_m_estim_1var_usr (g07dcc) (see Section 3.2.1.1 in the Essential Introduction).
2:     psifunction, supplied by the userExternal Function
psi must return the value of the weight function $\psi$ for a given value of its argument.
The specification of psi is:
 double psi (double t, Nag_Comm *comm)
1:     tdoubleInput
On entry: the argument for which psi must be evaluated.
2:     commNag_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 nag_robust_m_estim_1var_usr (g07dcc) you may allocate memory and initialize these pointers with various quantities for use by psi when called from nag_robust_m_estim_1var_usr (g07dcc) (see Section 3.2.1.1 in the Essential Introduction).
3:     isigmaIntegerInput
On entry: the value assigned to isigma determines whether $\stackrel{^}{\sigma }$ is to be simultaneously estimated.
${\mathbf{isigma}}=0$
The estimation of $\stackrel{^}{\sigma }$ is bypassed and sigma is set equal to ${\sigma }_{c}$.
${\mathbf{isigma}}=1$
$\stackrel{^}{\sigma }$ is estimated simultaneously.
4:     nIntegerInput
On entry: $n$, the number of observations.
Constraint: ${\mathbf{n}}>1$.
5:     x[n]const doubleInput
On entry: the vector of observations, ${x}_{1},{x}_{2},\dots ,{x}_{n}$.
6:     betadoubleInput
On entry: the value of the constant $\beta$ of the chosen chi function.
Constraint: ${\mathbf{beta}}>0.0$.
7:     thetadouble *Input/Output
On entry: if ${\mathbf{sigma}}>0$, then theta must be set to the required starting value of the estimate of the location argument $\stackrel{^}{\theta }$. A reasonable initial value for $\stackrel{^}{\theta }$ will often be the sample mean or median.
On exit: the $M$-estimate of the location argument $\stackrel{^}{\theta }$.
8:     sigmadouble *Input/Output
On entry: the role of sigma depends on the value assigned to isigma as follows.
If ${\mathbf{isigma}}=1$, sigma must be assigned a value which determines the values of the starting points for the calculation of $\stackrel{^}{\theta }$ and $\stackrel{^}{\sigma }$. If ${\mathbf{sigma}}\le 0.0$, then nag_robust_m_estim_1var_usr (g07dcc) will determine the starting points of $\stackrel{^}{\theta }$ and $\stackrel{^}{\sigma }$. Otherwise, the value assigned to sigma will be taken as the starting point for $\stackrel{^}{\sigma }$, and theta must be assigned a relevant value before entry, see above.
If ${\mathbf{isigma}}=0$, sigma must be assigned a value which determines the values of ${\sigma }_{c}$, which is held fixed during the iterations, and the starting value for the calculation of $\stackrel{^}{\theta }$. If ${\mathbf{sigma}}\le 0$, then nag_robust_m_estim_1var_usr (g07dcc) will determine the value of ${\sigma }_{c}$ as the median absolute deviation adjusted to reduce bias (see nag_median_1var (g07dac)) and the starting point for $\theta$. Otherwise, the value assigned to sigma will be taken as the value of ${\sigma }_{c}$ and theta must be assigned a relevant value before entry, see above.
On exit: the $M$-estimate of the scale argument $\stackrel{^}{\sigma }$, if isigma was assigned the value $1$ on entry, otherwise sigma will contain the initial fixed value ${\sigma }_{c}$.
9:     maxitIntegerInput
On entry: the maximum number of iterations that should be used during the estimation.
Suggested value: ${\mathbf{maxit}}=50$.
Constraint: ${\mathbf{maxit}}>0$.
10:   toldoubleInput
On entry: the relative precision for the final estimates. Convergence is assumed when the increments for theta, and sigma are less than ${\mathbf{tol}}×\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1.0,{\sigma }_{k-1}\right)$.
Constraint: ${\mathbf{tol}}>0.0$.
11:   rs[n]doubleOutput
On exit: the Winsorized residuals.
12:   nitInteger *Output
On exit: the number of iterations that were used during the estimation.
13:   commNag_Comm *Communication Structure
The NAG communication argument (see Section 3.2.1.1 in the Essential Introduction).
14:   failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

## 6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_BAD_PARAM
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_FUN_RET_VAL
The chi function returned a negative value: ${\mathbf{chi}}=⟨\mathit{\text{value}}⟩$.
NE_INT
On entry, ${\mathbf{isigma}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{isigma}}=0$ or $1$.
On entry, ${\mathbf{maxit}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{maxit}}>0$.
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}>1$.
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.
NE_REAL
On entry, ${\mathbf{beta}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{beta}}>0.0$.
On entry, ${\mathbf{tol}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{tol}}>0.0$.
NE_REAL_ARRAY_ELEM_CONS
All elements of x are equal.
NE_SIGMA_NEGATIVE
Current estimate of sigma is zero or negative: ${\mathbf{sigma}}=⟨\mathit{\text{value}}⟩$.
NE_TOO_MANY_ITER
Number of iterations required exceeds maxit: ${\mathbf{maxit}}=⟨\mathit{\text{value}}⟩$.
NE_ZERO_RESID
All winsorized residuals are zero.

## 7  Accuracy

On successful exit the accuracy of the results is related to the value of tol, see Section 5.

## 8  Parallelism and Performance

nag_robust_m_estim_1var_usr (g07dcc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_robust_m_estim_1var_usr (g07dcc) 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 Users' Note for your implementation for any additional implementation-specific information.

## 9  Further Comments

Standard forms of the functions $\psi$ and $\chi$ are given in Hampel et al. (1986)Huber (1981) and Marazzi (1987). nag_robust_m_estim_1var (g07dbc) calculates $M$-estimates using some standard forms for $\psi$ and $\chi$.
When you supply the initial values, care has to be taken over the choice of the initial value of $\sigma$. If too small a value is chosen then initial values of the standardized residuals $\frac{{x}_{i}-{\stackrel{^}{\theta }}_{k}}{\sigma }$ will be large. If the redescending $\psi$ functions are used, i.e., $\psi =0$ if $\left|t\right|>\tau$, for some positive constant $\tau$, then these large values are Winsorized as zero. If a sufficient number of the residuals fall into this category then a false solution may be returned, see page 152 of Hampel et al. (1986).

## 10  Example

The following program reads in a set of data consisting of eleven observations of a variable $X$.
The psi and chi functions used are Hampel's Piecewise Linear Function and Hubers chi function respectively.
Using the following starting values various estimates of $\theta$ and $\sigma$ are calculated and printed along with the number of iterations used:
 (a) nag_robust_m_estim_1var_usr (g07dcc) determined the starting values, $\sigma$ is estimated simultaneously. (b) You must supply the starting values, $\sigma$ is estimated simultaneously. (c) nag_robust_m_estim_1var_usr (g07dcc) determined the starting values, $\sigma$ is fixed. (d) You must supply the starting values, $\sigma$ is fixed.

### 10.1  Program Text

Program Text (g07dcce.c)

### 10.2  Program Data

Program Data (g07dcce.d)

### 10.3  Program Results

Program Results (g07dcce.r)

nag_robust_m_estim_1var_usr (g07dcc) (PDF version)
g07 Chapter Contents
g07 Chapter Introduction
NAG Library Manual