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maximum likelihood did not converge for gevfit

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I am trying to fit a generalized extreme value distribution usinf the functioin of "gevfit" as well as the "fitdist" to a rainfall data set which is as follows
1028.5
860.3
1088.2
711.6
874.3
776.2
973.7
457.9
pdGEV=fitdist(sample,'Generalizedextremevalue'); % probability distribution object
paraGEV= gevfit(sample); % paramter estimation
Parameters are calculated are as follows
'shape' k = -1.03801443465120 'scale' sigma = 273.595539473463 'location' mu = 824.624147545310
but there is a warning message
Warning: Maximum likelihood estimation did not converge. Function evaluation limit exceeded.
> In gevfit (line 115)
In prob.GeneralizedExtremeValueDistribution.fit (line 170)
In fitdist>localfit (line 238)
In fitdist (line 185)
In newcode (line 271)
Warning: Maximum likelihood has converged to a boundary point of the parameter space.
Confidence intervals and standard errors cannot be computed reliably.
> In gevfit (line 127)
In prob.GeneralizedExtremeValueDistribution.fit (line 170)
In fitdist>localfit (line 238)
In fitdist (line 185)
In newcode (line 271)
Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 7.552473e-21.
> In gevlike (line 94)
In prob.GeneralizedExtremeValueDistribution.fit (line 171)
In fitdist>localfit (line 238)
In fitdist (line 185)
In newcode (line 271)
Warning: Maximum likelihood estimation did not converge. Function evaluation limit exceeded.
> In gevfit (line 115)
In newcode (line 272)
Warning: Maximum likelihood has converged to a boundary point of the parameter space.
Confidence intervals and standard errors cannot be computed reliably.
> In gevfit (line 127)
In newcode (line 272)
How do I solve this problem?

Accepted Answer

the cyclist
the cyclist on 5 Sep 2019
I don't know for sure, but my best guess is that the problem is the relatively small amount of data, coupled with the fact that the GEV is difficult to fit because it has so much flexibility. (And the data don't really fit the shape, I think, but that is difficult to say with so little data.)
Can you use just the (non-generalized) extreme value distribution, or the Weibull distribution, for your application? Those both converge, presumably to sensible answers.
  3 Comments
Nikhilesh Gandhre
Nikhilesh Gandhre on 6 Sep 2019
But still if i need to fit gev to my data.can i do the same????considering the same data as mentioned above

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