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Topic: MLE of std dev for normal PDF skewed wrt confidence intervals
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Posts: 513
Registered: 2/23/10
MLE of std dev for normal PDF skewed wrt confidence intervals
Posted: Nov 12, 2012 5:36 PM
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I am using the code below to get familiar with Maximum Likelihood
Estimation. I am fitting to a normal distribution. Following the MLE
estimate, comparison is made with (i) mean() & std(); (ii) normfit();
and (iii) MLE estimate of the error of the estimates.

I'd like to get the feedback of more experienced MLE users. For
parameter 2 (sigma, or standard deviation), the estimate is 0.4684,
with 95% confidence intervals at 0.3311 and 1.0192. The estimate is
much closer to the lower end of the confidence interval. I understand
that that PDF for the estimate of sigma may be heavily skewed. Would
this be the explanation? The reason I ask is because a paper I am
studying uses the MLE estimate of the error on sigma, which leads to a
+/- delta_sigma, which basically pretends that the confidence
intervals are symmetric around the estimate. The above skewing of
estimated sigma relative to the confidence intervals means that I have
to be careful and ensure that this assumption wasn't carried through
to all the calculations that depend on confidence intervals.


Test code

disp(['data=' num2str(data)]);
disp(['Ndata=' num2str(Ndata)]);

disp(' ')

% MLE of normal distribution parameters
disp('Parameter_hat (''phat'' for mean, std dev) &')
disp('95% parameter confidence intervals ''pci'':')
[phat pci] = mle(data)

disp('Offsets, confidence interval bounds from estimate:')
disp( pci - [phat; phat] )

disp(' ')

% Compare with 'usual' estimations
disp('[ mean(data) & std(data,1) ]:')
disp([ mean(data) std(data,1) ])

disp(' ')

% Compare with 'normfit'
[mu,sigma,muci,sigmaci] = normfit(data)

disp(' ')

% Siegmund Brandt's 2nd derivative of log-likelihood wrt parameters
disp('From MLE approach:')
fprintf( 'std dev of Mu=%g\n', phat(2)/sqrt(Ndata) );
fprintf( 'std dev of StdDev=%g\n', phat(2)/sqrt(2*Ndata) );

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