Weikko Jaross
June 8th, 2001, 05:33 PM
I am very new to this parameter estimation stuff, so any and all advice I will appreciate.
I am working with data that of all things represents elements of cycle time observations for logging machinery (time to success, ha! ha!).
For example, I am taking the time observations (sec) for setting a choker and attempting to fit distributions to these observations using Weibill 6. Say for 30 to 1044 independent observations, depending on how I stratify my data.
I have been working under the impression that the goodness of fit is very important, esp. if I want to demonstrate that I know what I am doing to my MS committee. Also, I am not psyched about making the wrong choice of population distribution. This would not help be successful.
When I first got started, I was estimating parameters using the distribution wizard with the MLE technique. I then went through each individual distribution and viewed the goodness of fit results for both the KS and X^2. I got horrible results, in sum cases 99% for both and for all distributions.
SO then I got the bright idea to begin grouping the data by progressively larger buckets in an attempt to smooth the data until I got a KS < 5%.
I would like some advice on how I really should be proceeding.
I am working with data that of all things represents elements of cycle time observations for logging machinery (time to success, ha! ha!).
For example, I am taking the time observations (sec) for setting a choker and attempting to fit distributions to these observations using Weibill 6. Say for 30 to 1044 independent observations, depending on how I stratify my data.
I have been working under the impression that the goodness of fit is very important, esp. if I want to demonstrate that I know what I am doing to my MS committee. Also, I am not psyched about making the wrong choice of population distribution. This would not help be successful.
When I first got started, I was estimating parameters using the distribution wizard with the MLE technique. I then went through each individual distribution and viewed the goodness of fit results for both the KS and X^2. I got horrible results, in sum cases 99% for both and for all distributions.
SO then I got the bright idea to begin grouping the data by progressively larger buckets in an attempt to smooth the data until I got a KS < 5%.
I would like some advice on how I really should be proceeding.