Re: variance of difference sign test
- To: mathgroup at smc.vnet.net
- Subject: [mg65197] Re: variance of difference sign test
- From: ab_def at prontomail.com
- Date: Fri, 17 Mar 2006 05:45:37 -0500 (EST)
- References: <duudcl$hug$1@smc.vnet.net>
- Sender: owner-wri-mathgroup at wolfram.com
Suppose that we have n independent identically distributed random variables {u[1], ..., u[n]} and P[u[i] == u[j]] == 0 for i != j. We form another sequence {xi[1] = Boole[u[1] > u[2]], ..., xi[n - 1] = Boole[u[n - 1] > u[n]]} and we're looking for the variance of the sum of xi[i]: D[N[n]] == Variance[Sum[xi[i], {i, n - 1}]] == Variance[Sum[xi[i], {i, n - 2}] + xi[n - 1]] == Variance[Sum[xi[i], {i, n - 2}]] + Variance[xi[n - 1]] + 2*Covariance[Sum[xi[i], {i, n - 2}], xi[n - 1]] == D[N[n - 1]] + 1/4 + 2*Sum[Covariance[xi[i], xi[n - 1]], {i, n - 2}] For any pair of adjacent elements we have Covariance[xi[1], xi[2]] == P[xi[1] == 1 && xi[2] == 1] - P[xi[1] == 1]*P[xi[2] == 1] == P[u[1] > u[2] > u[3]] - P[u[1] > u[2]]*P[u[2] > u[3]] == 1/6 - 1/4 == -1/12 because all permutations of {u[1], ..., u[n]} are equally probable. For any non-adjacent elements Covariance[xi[i], xi[j]] == 0. Therefore, D[N[n]] == D[N[n - 1]] + 1/4 + 2*(-1/12), D[N[2]] = 1/4 and D[N[n]] == (n + 1)/12 if n >= 2. Here is a check for n = 6: In[1]:= n = 6; Lvalfreq = {First@ #, Length@ #}& /@ Split@ Sort@ (Count[Sign[Most@ # - Rest@ #], 1]& /@ Permutations@ Range@ n) {Lval, Lp} = {Lvalfreq[[All, 1]], Lvalfreq[[All, 2]]/n!}; mu = Lval.Lp sigma = ((Lval - mu)^2).Lp Out[2]= {{0, 1}, {1, 57}, {2, 302}, {3, 302}, {4, 57}, {5, 1}} Out[4]= 5/2 Out[5]= 7/12 And a numerical test: In[6]:= Lcnt = Array[ Count[Sign[Most@ # - Rest@ #]&@ Array[Random[]&, n], 1]&, 10^5]; {Mean@ Lcnt, Variance@ Lcnt} - {mu, sigma} // N Out[7]= {0.00262, 0.0033856695} Maxim Rytin m.r at inbox.ru Darren Glosemeyer wrote: > > For the variance quoted on the TimeSeries page, I initially thought the > same thing you did. Assuming the signs are independent and there are equal > probabilities of getting positive and negative signs (and 0 probability of > getting a 0 difference), the statistic would follow > BinomialDistribution[n-1, 1/2], which would have a variance of > (n-1)/4. Simulations give a variance that appears to be (n+1)/12 (which > would still indicate a typo in the TimeSeries documentation). I haven't > figured out why this should be the variance yet. My best guess is that the > assumption of independence is not valid given the differencing and as a > result the distribution is something other than BinomialDistribution[n-1, 1/2]. > > > Darren Glosemeyer > Wolfram Research > > > At 05:15 AM 3/10/2006 -0500, john.hawkin at gmail.com wrote: > >Hello, > > > >I have two questions. > > > >1. Are there any resources of .nb files available on the internet > >where I might find an implementation of the D'Agostino Pearson k^2 test > >for normal variates? > > > >2. In the mathematica time series package (an add-on), the > >"difference-sign" test of residuals is mentioned (url: > >http://documents.wolfram.com/applications/timeseries/UsersGuidetoTimeSeries/1.6.2.html). > > It says that the variance of this test is (n+1) / 2. However, it > >would seem to me that a simple calculation gives a variance of (n-1)/4. > > It goes as follows: > > > >If the series is differenced once, then the number of positive and > >negative values in the difference should be approximately equal. If Xi > >denotes the sign of each value in the differenced series, then > >Mean(Xi) = 0.5(1) + 0.5(0) = 0.5 > >Var(Xi) = Expectation( (Xi - Mean(Xi))^2 ) > >= Expectation( Xi^2 -Xi + 0.25 ) > >= 0.5 - 0.5 + 0.25 > >= 0.25 > > > >And assuming independence of each sign from the others, the total > >variance should be the sum of the individual variances, up to n-1 for n > >data points (since there are only n-1 changes in sign), thus > > > >Variance = (n-1) / 4 > > > >There is an equivalent problem in Lemon's "Stochastic Physics" about > >coin flips, for which the answer is listed, without proof, as (n-1)/8. > >Because of these three conficting results I am wondering if I have made > >an error in my calculation, and if anyone can find one please let me > >know. > > > >Thank you very much, > > > >-John Hawkin
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