Recent content by neznam

Sorry,one more clarification the p's are between 0 and 1, so the log will not be negative.

Yes If X and Y are random variables and H(X) and G(Y) are functions of those random variables then what will be an expression of the COV(H(X), G(Y)) in terms of X and Y. Similar expression is Var[H(X)] equals approximately [H'(mean)]^2 * Var(X) Appreciate any help Thank you :-)

What I am looking for really is an expression for the covariance if I have a function of the random variable instead of only a random variable. The function is the log function above in the original question. I know there is such an expression for the variance of a function of the random...

I need to find an approximation of the covariance of a function of a random variable. \Theta1- log[p1/(1-p1)] where p1 is binomial \Theta2- log[p2/(1-p2)] where p2 is binomial I need to find the covariance of \Theta1 and \Theta2 Please- any help will be greatly appreciated

Thanks a lot. That makes a lot of sense now :-)

Hi, I have a conceptual question. Looking at exponential smoothing methods I came across relationship between the autocorrelation function and lambda. It says that if the time series doesn't apper to be autocorrelated then lambda is expected to have a low value :confused: .Any help will be...

m+b-(t1+N-1)b/N when t1<=T<=t1+N-2

Sorry it is not lower case n --just the same upper case N

Please any help will be greatly appreciated. Suppose that a simple moving average of span N is used to forecast a time series that varies randomly around a constant mean, that is yt=m (m-mean and yt is y sub t). At the start of the period t1 the process shifts to a new mean level, say, m+b...

Ok i think i got it E[x]=[(lnp)/p]*e(lnp)=lnp Thanks so much

sum of z^k/k! is e^k, so is it sum of z^k/(k-1)! will be e^(k-1)?

So my main problem is figuring out the E[X] and as a hint of this problem it is saying to use Maclaurin series.

E[x] = [1*(lnp)^1]/p*1!+[2*(lnp)^2]/p*2!+[3*(lnp)^3]/p*3!+... Once I have the expected value E[X] of this distribution I will be able to find the CRLB as well which is defined to be in this case 1/(n*[d/dp ln f(x;p)]^2 Any help is appreciated

Random Sampe of size n from distribution with pdf f(x;p)={(lnp)^x}/px! for x=0,1,...; p>1 and 0 otherwise Find CRLB for p? My problem is finding E[x] which is somekind of maclaurin series but can't figure out which one? Please any suggestions? Thanks