for a simulation I’ll need to simulate project delays.
I’ve data on 20k projects with delays in quarters. Some of them were finished ahead of plan (i.e. neg. delays) others with a delay of 1-16 quarters. I assume a delay of 0 for everything finished ahead of plan as this does not get awarded in my case. The histogram looks like an exponential distribution would be a good fit.
I want to differentiate the distribution parameter (ie. lamda) depending on multiple attributes/factors of the project (3-5 factors, eg. region (south, north, west, east), project manager (X, Y, Z), type (basic, premium), etc.)). So I’m wondering what is the best approach to estimate the matrix of coefficients?
Individual estimation (for each combinations of factors):
- Should I extract subsamples for each possible combination of factors and estimate the lambdas in a regular way for each (eg MLE, LSE, etc.)?
- What must hold, that I could run the parametrization on each dimension (eg region) independently and then somehow cross the coefficient vectors to get the matrix? (rather then going through all combinations)
- Which test would you recommend to identify the 1-2 most sensitive dimensions/factors for the delay distribution (would ANOVA work assuming an exp. distribution and 5-8 groups each dimension)?
- Any method allowing to run a multi-dimensional parametrization on the whole data set and will this be more accurate?
Many thanks for any input and ideas! Serge