On Wed, 26 Apr 2000 20:43:02 -0400, Greg Heath
<[EMAIL PROTECTED]> wrote:
> Can you help or lead me to the appropriate reference?
>
> I have 526 radar measurements evenly sampled over 26.25 sec (i.e., pulse
> repetition frequency = 20 points per second).
>
> mean = 0.0
> stdv = 1.2
> t0 = 1 sec (1/e decorrelation time from the autocorrelation function)
>
> I want to test the null hypothesis that these correlated measurements
> could have been drawn from a zero-mean Gaussian distribution.
The usual, simple alternative would say, mean not-zero. Since your
mean is observed to be zero, I guess you can accept that simple null.
> However I don't believe I have enough independent measurements.
>
> Will bootstrapping help? i.e.,
< snip, example that is probably irrelevant >
Well, bootstrapping is a real problem when you don't know what to do
with that serial correlation. And you don't, do you?
So, why do you care about normality? If you toss the numbers up and
do a test as if they were independent, what do you get? -- that gives
you one limit. That is, if they look Normal *despite* the odd values
that you might have because of serial correlation, then you can accept
this set as robustly Normal, in regards to whatever you are testing.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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