nag_estim_weibull (g07bec) (PDF version)
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g07 Chapter Introduction
NAG C Library Manual

NAG Library Function Document

nag_estim_weibull (g07bec)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_estim_weibull (g07bec) computes maximum likelihood estimates for arguments of the Weibull distribution from data which may be right-censored.

2  Specification

#include <nag.h>
#include <nagg07.h>
void  nag_estim_weibull (Nag_CestMethod cens, Integer n, const double x[], const Integer ic[], double *beta, double *gamma, double tol, Integer maxit, double *sebeta, double *segam, double *corr, double *dev, Integer *nit, NagError *fail)

3  Description

nag_estim_weibull (g07bec) computes maximum likelihood estimates of the arguments of the Weibull distribution from exact or right-censored data.
For n realizations, yi, from a Weibull distribution a value xi is observed such that
xiyi.
There are two situations:
(a) exactly specified observations, when xi=yi
(b) right-censored observations, known by a lower bound, when xi<yi.
The probability density function of the Weibull distribution, and hence the contribution of an exactly specified observation to the likelihood, is given by:
fx;λ,γ=λγxγ-1exp-λxγ,  x>0,   for ​λ,γ>0;
while the survival function of the Weibull distribution, and hence the contribution of a right-censored observation to the likelihood, is given by:
Sx;λ,γ=exp-λ xγ,   x> 0,   for ​ λ ,γ> 0.
If d of the n observations are exactly specified and indicated by iD and the remaining n-d are right-censored, then the likelihood function, Like ​λ,γ is given by
Likeλ,γλγd iDxiγ-1 exp-λi=1nxiγ .
To avoid possible numerical instability a different parameterization β,γ is used, with β=logλ. The kernel log-likelihood function, Lβ,γ, is then:
Lβ,γ=dlogγ+dβ+γ-1iDlogxi-eβi=1nxiγ.
If the derivatives L β , L γ , 2L β2 , 2L β γ  and 2L γ2  are denoted by L1, L2, L11, L12 and L22, respectively, then the maximum likelihood estimates, β^ and γ^, are the solution to the equations:
L1β^,γ^=0 (1)
and
L2β^,γ^=0 (2)
Estimates of the asymptotic standard errors of β^ and γ^ are given by:
seβ^=-L22 L11L22-L122 ,  seγ^=-L11 L11L22-L122 .
An estimate of the correlation coefficient of β^ and γ^ is given by:
L12L12L22 .
Note:  if an estimate of the original argument λ is required, then
λ^=expβ^  and  seλ^=λ^seβ^.
The equations (1) and (2) are solved by the Newton–Raphson iterative method with adjustments made to ensure that γ^>0.0.

4  References

Gross A J and Clark V A (1975) Survival Distributions: Reliability Applications in the Biomedical Sciences Wiley
Kalbfleisch J D and Prentice R L (1980) The Statistical Analysis of Failure Time Data Wiley

5  Arguments

1:     censNag_CestMethodInput
On entry: indicates whether the data is censored or non-censored.
cens=Nag_NoCensored
Each observation is assumed to be exactly specified. ic is not referenced.
cens=Nag_Censored
Each observation is censored according to the value contained in ic[i-1], for i=1,2,,n.
Constraint: cens=Nag_NoCensored or Nag_Censored.
2:     nIntegerInput
On entry: n, the number of observations.
Constraint: n1.
3:     x[n]const doubleInput
On entry: x[i-1] contains the ith observation, xi, for i=1,2,,n.
Constraint: x[i-1]>0.0, for i=1,2,,n.
4:     ic[dim]const IntegerInput
Note: the dimension, dim, of the array ic must be at least
  • n when cens=Nag_Censored;
  • 1 otherwise.
On entry: if cens=Nag_Censored, then ic[i-1] contains the censoring codes for the ith observation, for i=1,2,,n.
If ic[i-1]=0, the ith observation is exactly specified.
If ic[i-1]=1, the ith observation is right-censored.
If cens=Nag_NoCensored, then ic is not referenced.
Constraint: if cens=Nag_Censored, then ic[i-1]=0 or 1, for i=1,2,,n.
5:     betadouble *Output
On exit: the maximum likelihood estimate, β^, of β.
6:     gammadouble *Input/Output
On entry: indicates whether an initial estimate of γ is provided.
If gamma>0.0, it is taken as the initial estimate of γ and an initial estimate of β is calculated from this value of γ.
If gamma0.0, then initial estimates of γ and β are calculated, internally, providing the data contains at least two distinct exact observations. (If there are only two distinct exact observations, then the largest observation must not be exactly specified.) See Section 8 for further details.
On exit: contains the maximum likelihood estimate, γ^, of γ.
7:     toldoubleInput
On entry: the relative precision required for the final estimates of β and γ. Convergence is assumed when the absolute relative changes in the estimates of both β and γ are less than tol.
If tol=0.0, then a relative precision of 0.000005 is used.
Constraint: machine precisiontol1.0 or tol=0.0.
8:     maxitIntegerInput
On entry: the maximum number of iterations allowed.
If maxit0, then a value of 25 is used.
9:     sebetadouble *Output
On exit: an estimate of the standard error of β^.
10:   segamdouble *Output
On exit: an estimate of the standard error of γ^.
11:   corrdouble *Output
On exit: an estimate of the correlation between β^ and γ^.
12:   devdouble *Output
On exit: the maximized kernel log-likelihood, Lβ^,γ^.
13:   nitInteger *Output
On exit: the number of iterations performed.
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 value had an illegal value.
NE_CONVERGENCE
Iterations have failed to converge in value iterations.
NE_DIVERGENCE
Iterations have diverged.
NE_INITIALIZATION
Unable to calculate initial values.
NE_INT
On entry, n=value.
Constraint: n1.
NE_INT_ARRAY_ELEM_CONS
On entry, element value of ic was not valid. ic[i]=value.
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_OBSERVATIONS
On entry, there are no exact observations.
NE_OVERFLOW
Potential overflow detected.
NE_REAL
On entry, tol is invalid: tol=value.
NE_REAL_ARRAY_ELEM_CONS
On entry, observation value is 0.0. x[i]=value.
NE_SINGULAR
Hessian matrix is singular.

7  Accuracy

Given that the Weibull distribution is a suitable model for the data and that the initial values are reasonable the convergence to the required accuracy, indicated by tol, should be achieved.

8  Further Comments

The initial estimate of γ is found by calculating a Kaplan–Meier estimate of the survival function, S^x, and estimating the gradient of the plot of log-logS^x against x. This requires the Kaplan–Meier estimate to have at least two distinct points.
The initial estimate of β^, given a value of γ^, is calculated as
β^=logdi=1nxiγ^ .

9  Example

In a study, 20 patients receiving an analgesic to relieve headache pain had the following recorded relief times (in hours):
1.1 1.4 1.3 1.7 1.9 1.8 1.6 2.2 1.7 2.7 4.1 1.8 1.5 1.2 1.4 3.0 1.7 2.3 1.6 2.0
(See Gross and Clark (1975).) This data is read in and a Weibull distribution fitted assuming no censoring; the parameter estimates and their standard errors are printed.

9.1  Program Text

Program Text (g07bece.c)

9.2  Program Data

Program Data (g07bece.d)

9.3  Program Results

Program Results (g07bece.r)


nag_estim_weibull (g07bec) (PDF version)
g07 Chapter Contents
g07 Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012