09-01-2015
rs_poetic
Calcite | Level 5
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04-24-2012
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Latest posts by rs_poetic
Subject Views Posted 1626 06-13-2013 09:17 AM 1682 06-11-2013 01:11 PM 1520 04-24-2012 01:33 PM 1520 04-24-2012 12:18 PM 1730 04-24-2012 11:17 AM -
Activity Feed for rs_poetic
- Posted Re: regression on order statistics on Statistical Procedures. 06-13-2013 09:17 AM
- Posted regression on order statistics on Statistical Procedures. 06-11-2013 01:11 PM
- Posted Re: getting started with SEVERITY on SAS Forecasting and Econometrics. 04-24-2012 01:33 PM
- Posted Re: getting started with SEVERITY on SAS Forecasting and Econometrics. 04-24-2012 12:18 PM
- Posted getting started with SEVERITY on SAS Forecasting and Econometrics. 04-24-2012 11:17 AM
06-11-2013
01:11 PM
In my problem, n bidders place $ bids for unopened lots. They are basing their bids on their judgement of the value of the contents. With bids sorted in order, we have n order statistics, repeated for m lots. I am assuming that a single distribution type generates the bidding, e.g. lognormal or the like. In reality the mean and variance would likely be different for each lot, however to simplify initially I am willing to divide each lot's bids by its high bid. E.g. lot A might have "standardized" order statistics high to low {1, .9, .8, .75, .70. .5,...}. I wish to select a distribution type and its parameters based on these "standardized" sets or order statistics. PROC UNIVARIATE provides Q-Q plots and methods that readily identify the best distribution and good parameter estimates for each lot. e.g., the Q-Q plot of lot A might be shown to have the best fit to lognormal(.5,1.1).. Of course, if I apply this method to each lot in turn, I get m different results. What I wish is the parameter estimates that are the best fit to data for all lots. Is there a built-in procedure in SAS that does that? or am I on my own to specify a nonlinear regression?
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04-24-2012
01:33 PM
Thanks!!!
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04-24-2012
12:18 PM
Thank you. The MODEL statement does look peculiar but perhaps that is due to the context being to fit a predefined univariate probability distribution. However, the example in the documentation on page 1 of "A simple Example of Fitting Predefined Distributions" is clear that "the MODEL statement specifies the variable to be modeled along with the model selection criterion" and I copied the example there exactly (I believe). Can you provide an illustrative little program for proc severity that does not include a MODEL statement?
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04-24-2012
11:17 AM
Hello, Proc Severity, an experimental proc in ETS, sounds like the answer to my prayers, but I am stuck at the outset with what seems like a very simple error message. I am trying to run the example given in the documentation, simply: proc severity data=spilldat; model spill / crit = aicc; run; where spill is a variable in the data. Although the proc begins to run, the immediate error message in the log is: 180 errror 180-322: statement is not valid or it is used out of order Please advise. (I have ETS as part of 9.3 TS level 1M1 on Windows 7.)
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