Test for randomness

Discussion in 'Probability' started by Joe Avery, Oct 15, 2010.

  1. Joe Avery

    Joe Avery Guest

    Pls excuse my naive questioning.

    If you have a set of measurements for a random variable X and you take
    the mean and standard deviation is the ratio (mean/std. dev) a
    measure of randomness? If not, what is it?
     
    Joe Avery, Oct 15, 2010
    #1
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  2. Joe Avery

    Henry Guest

    It is the inverse of the "coefficient of variation"
    http://en.wikipedia.org/wiki/Coefficient_of_variation
    and probably only meaningful for positive random variables
     
    Henry, Oct 16, 2010
    #2
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  3. Joe Avery

    Ray Koopman Guest

    It's an "effect size", the standardized distance of the mean from 0,
    a quick and dirty measure of the extent to which the distribution
    overlaps 0.
     
    Ray Koopman, Oct 16, 2010
    #3
  4. Joe Avery

    Tom K Guest

    With Maurice Allais' discovery of his Theorem T (c. 1985) there can be no
    definitive test for randomness.
     
    Tom K, Oct 17, 2010
    #4
  5. Joe Avery

    Joe Avery Guest

    I cannot find this theorem anywhere.

    An example of my problem: I trade stocks and I sometines win and
    sometimes lose. I call the value Ti. Given a large i, > 1000, is there
    any way to determines whether these values are random or there is a
    certain strategy behind them? In other words, is there a way to test
    if the Ti's could have been obtained by chance?
     
    Joe Avery, Oct 22, 2010
    #5
  6. Joe Avery

    Tom K Guest

    Allais wrote a contribution to a collection of articles on causality.
    Unfortunately I do not have the reference info: Arts Library @ University
    of Waterloo.
    Given a particular model and probability distribution one may be able to
    test different hypotheses. But whatever hypotheses you wish to test you
    should consider that there is a maximum that you can lose on any stock and
    that stock prices may not be independent of one another.
     
    Tom K, Oct 22, 2010
    #6
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