On Tuesday 01 July 2003 05:16, M. Edward Borasky wrote:
Unfortunately, the data are *non-negative*, not strictly positive. Zero is
a valid and frequent inter-arrival time. It is, IIRC, the most likely value
of a (negative) exponential distribution.
Not really. Zero+ is the value with highest
the two parts into
Y = p * 0 + (1-p) * X
where p is the proportion of 0's, and X represents the continuous
component of the random variable.
I must amend myself... what I should have written is
Y = I * 0 + (1-I) * X
where I is a Bernoulli random variable with probability p of success
On Sun, 29 Jun 2003, M. Edward Borasky wrote:
I have a collection of data which includes inter-arrival times of requests
to a server. What I've done so far with it is use sm.density to explore
the distribution, which found two large peaks. However, the peaks are made
up of Gaussians, and
On Monday 30 June 2003 01:23, M. Edward Borasky wrote:
I have a collection of data which includes inter-arrival times of requests
to a server. What I've done so far with it is use sm.density to explore
the distribution, which found two large peaks. However, the peaks are made
up of Gaussians,
Unfortunately, the data are *non-negative*, not strictly positive. Zero is a
valid and frequent inter-arrival time. It is, IIRC, the most likely value of
a (negative) exponential distribution.
--
M. Edward (Ed) Borasky
mailto:[EMAIL PROTECTED]
http://www.borasky-research.net
Suppose that
Thanks!! It does look like the easiest thing is direct ML; the code for a
normal mixture is in the book, so all I have to do is modify that for a sum
of a hyper-exponential, for which I have an approximate mean and CV, and a
normal, for which I have an approximate mean and SD.
I have two big
I have a collection of data which includes inter-arrival times of requests
to a server. What I've done so far with it is use sm.density to explore
the distribution, which found two large peaks. However, the peaks are made
up of Gaussians, and that's not really correct, because the inter-arrival
