06-16-2021
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Obsidian | Level 7
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06-25-2016
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Subject Views Posted 1116 12-29-2018 07:30 PM 1364 03-15-2018 09:27 AM 1390 03-15-2018 07:52 AM 5508 08-24-2017 09:30 AM 5523 08-24-2017 05:56 AM 5535 08-24-2017 03:27 AM 5552 08-23-2017 07:29 PM 5554 08-23-2017 07:25 PM 5612 08-23-2017 06:07 AM 2251 08-01-2017 06:20 AM -
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- Posted Built-in probability distribution in PROC GENMOD on New SAS User. 12-29-2018 07:30 PM
- Tagged Built-in probability distribution in PROC GENMOD on New SAS User. 12-29-2018 07:30 PM
- Tagged Built-in probability distribution in PROC GENMOD on New SAS User. 12-29-2018 07:30 PM
- Posted Re: Correct for Heteroskedasticity with PROC REG on SAS Programming. 03-15-2018 09:27 AM
- Posted Correct for Heteroskedasticity with PROC REG on SAS Programming. 03-15-2018 07:52 AM
- Tagged Correct for Heteroskedasticity with PROC REG on SAS Programming. 03-15-2018 07:52 AM
- Tagged Correct for Heteroskedasticity with PROC REG on SAS Programming. 03-15-2018 07:52 AM
- Posted Re: Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-24-2017 09:30 AM
- Posted Re: Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-24-2017 05:56 AM
- Liked Re: Modelling data using inflated beta regression and PROC NLMIXED for Rick_SAS. 08-24-2017 05:51 AM
- Posted Re: Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-24-2017 03:27 AM
- Posted Re: Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-23-2017 07:29 PM
- Liked Re: Modelling data using inflated beta regression and PROC NLMIXED for StatDave. 08-23-2017 07:26 PM
- Posted Re: Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-23-2017 07:25 PM
- Posted Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-23-2017 06:07 AM
- Tagged Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-23-2017 06:07 AM
- Tagged Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-23-2017 06:07 AM
- Tagged Modelling data using inflated beta regression and PROC NLMIXED on Statistical Procedures. 08-23-2017 06:07 AM
- Tagged Fitting exponential distributed data and overlaying empirical with theoretical data on SAS Programming. 08-01-2017 06:21 AM
- Tagged Fitting exponential distributed data and overlaying empirical with theoretical data on SAS Programming. 08-01-2017 06:21 AM
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Posts I Liked
Subject Likes Author Latest Post 1 3 1 1
12-29-2018
07:30 PM
Hi all, I'm trying to understand what is the meaning of 'Built-in probability distribution' and the assumption behind in the PROC GENMOD framework. Particularly, in the MODEL statement, one can use the DIST= option, where the DIST string can assume the following categories: BINOMIAL; GAMMA; GEOMETRIC; ... as suggested in the SAS Documentation. In the case one omits such distribution, the PROC assumes automatically that the distribution is normal. Now, such distribution assumption implies that the data underlying the model are distributed according to the selected distribution? For instance, in the case one sets DIST=BINOMIAL and LINK=LOGIT one implicitly assumes that the dependent variable is binomial distributed? This holds in a bayesian framework too? Thanks all for the help in advance!
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03-15-2018
09:27 AM
Thanks for replying @Miracle, but I cannot do that! I need to correct it by applying the heteroscedasticity-consistent parameter estimates. I can doit by using another PROC too.
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03-15-2018
07:52 AM
Hi all SAS users, I'm running a linear regression model by using PROC REG (v. SAS 9.4). The model did not overcome the test about heteroschedasticity and I want to correct for the presence of heteroschedastic errors. Browsing on the internet I didn't find anything useful to do that. Can you provide a way, like, for instance, a statement, to do it? Thanks for the help!
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- Tags:
- proc reg
- regression
08-24-2017
09:30 AM
v_lgd_da_mora_st_out is the response variable while the other ones are the independent variables; sorry for the superficiality.
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08-24-2017
05:56 AM
Thanks @Rick_SAS for providing the link to the article about the initial guess computation. Really useful! Anyway, I attached a sample dataset with 1000 records and the variables used in the programs; it would be really appreciated if you could show me how to do that with an example. Thanks again!
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08-24-2017
03:27 AM
Hi @Rick_SAS, according to me, the paper I posted in the link and the relative code show how to estimate the parameter of the inflated beta regression, while I referred to the input parameters pie, kesai and phi. Such ones are the parameters of the distributions and in the paper are given. I am asking for a way to get such values from data because I did not understand how to do that from the paper. Thanks.
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08-23-2017
07:29 PM
Hi @StatDave and thanks for the reply. Actually my problem is to understand how I can estimate such parameters and not to understand how to use PROC NLMIXED. Anyway, if you suggest an example code by using the PROC FMM it will be surely appreciated. Thanks!
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08-23-2017
07:25 PM
Thanks for the answer @Rick_SAS but I meant about the way to estimate them, assuming they were something like the distribution parameters. According to you, is there a way to extrapolate such values from data?
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08-23-2017
06:07 AM
Hi all SAS users, I'm trying to model loss-given-default data on the basis of the SAS paper n. 1593-2014 (see http://support.sas.com/resources/papers/proceedings14/1593-2014.pdf). Particularly, the third model proposed by the authors in such paper is based on the inflated beta regression model, that is explained with the following SAS program: proc nlmixed data=MyData tech=quanew maxiter=3000 maxfunc=3000 qtol=0.0001;
parms b0-b14=0.0001
pie=0.2
kesai=0.3
phi=2;
cov_mu=b0+b1*Var1+b2*Var2+…+b14*Var14;
mu=logistic(cov_mu);
if RR=0
then loglikefun=log(pie)+log(1-kesai);
if RR>=1
then loglikefun=log(pie)+log(kesai);
if 0<RR<1
then loglikefun=log(1-pie)+lgamma(phi)-lgamma(mu*phi)-lgamma((1-mu)*phi)
+(mu*phi-1)*log(RR)+((1-mu)*phi-1)*log(1-RR);
predict pie*kesai+(1-pie)*mu out=Inf_beta_output (keep=instrument_id RR pred);
model RR~general(loglikefun);
run; I would like to understand what are the parameters pie, kesai and phi and how they can be computed/estimated. Indeed, I know that the beta distribution has 2 shape parameters, usually named alpha and beta, that can be computed on the basis of the sample mean and variance by using the method of moments, but I do not know how to to relate such parameters with ones used in the program (pie, kesai and phi). Could you suggest some solution or explanation about such parameters? Thanks all!
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08-01-2017
06:20 AM
Hi all, By using SEVERITY procedure, I found that the exponential distribution is the one that fits better empirical data; so, I got the parameter theta. I built a random dataset distributed according to an exponential distribution wiith parameter theta equal to 0.7423, on the basis of the following code: data exponential (keep = x);
call streaminit(&seed.);
theta = &theta.;
do i = 1 to &n.;
x = theta * rand("Exponential");
output;
end;
run; where n, theta estimate and seed are previosly defined. After that, I merge the empirical data with the ones etimated above. Now, I want to compare graphically the distributions. Can you suggest a way to overlay them? Thanks all!
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06-16-2017
06:09 AM
HI @Rick_SAS and thanks for your answer. I have to compute the KS stat and the relative p-value comparing the the theoretical Poisson distribution with the observed data. I agree with you about the fact it does not make sense, but it is a request for reporting the validation results of internal models; the aim is to quantify a distance between the empirical distribution function and the cdf of the Poisson distribution. By using the PROC GENMOD, as you suggested above, I did not get the KS statistic and p-value. Could you suggest some way to run the KS test? Thanks!
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06-16-2017
05:34 AM
Thanks for your answer @Ksharp! Do you mean I could simply use the UNIVARIATE procedure to implement the KS test? Particularly, I may use: proc npar1way
edf
data = dataset;
class x
var y;
exact ks;
run; where y is the observed data and x is a vector of simulated data coming from a Poisson distribution? Thanks!
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06-15-2017
09:18 AM
Hello SAS users, I used the SEVERITY procedure to fit and test data dstribution. Among all the stats and plots available, one can have the critical values for the Anderson-Darling test. Now, I wonder about a way to compute the relative p-value for such statistics/test. Can you suggest a way to compute that? The probability distribution is the Geenralyzed Pareto distribution. Thanks all in advance for the help!!
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06-15-2017
03:56 AM
Thanks !!
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06-15-2017
03:54 AM
Hello SAS user, I have to run the Kolmogorov-Smirnov test on a Poisson distributed data, by quantifying the distance between the empirical distribution function of the loss data set and the cumulative Poisson distribution function; in my case, the Poisson distribution function is the reference parametric distribution. Browsing on the internet, till now I found only the KS test special case in which one compares the Normal distribution vs the empirical one. What about the case I need to compare the empirical distribution function against the Poisson distribution? Thanks all in advance for your help!
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