About Azeddine

Azeddine · ‎09-20-2016

To be honnest it gave me result but the result are different between resimulation with proc univariate and your resimulation.

Azeddine · ‎09-20-2016

Thank you for your answer, in my case it doesn't work, So i try with a analytic inversion of function: The initial Function of GPD is defined here in proc severity: http://support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/viewer.htm#etsug_severity_sect015.htm with x=(x'-Treshold) I use this formula : X'=&Treshold+(((1-rand('uniform'))**(-&&Gpd_xi))-1)*(&&Gpd_theta/&&Gpd_xi)

Azeddine · ‎09-14-2016

Hello, I use Proc severity and i have parameter for GPD distribution, I would like to use them to simulate an sampling of GPD like a "rand("Weib", 0.25,2.1)" My parameters are for theta =126998.5 and for xi =0.31954.

Azeddine · ‎09-07-2016

I'm talking about distribution, Instead of Fitquantiles default percentile(exemple inside the /**/ in the code), i would like to change them, I need necessarely to use quantile function? I can't do that by Proc univariate? proc univariate data=Grave; var VAR1; histogram / Exponential(theta=0); qqplot / Exponential(theta=0 Sigma=est); ppplot / Exponential(theta=0 Sigma=est); histogram / gamma(theta=0); qqplot / gamma(theta=0 alpha=est Sigma=est); ppplot / gamma(theta=0 alpha=est Sigma=est); ODS output FitQuantiles=Quantiles&i. /*Percent= 20 40 99.5*/; run;

Azeddine · ‎09-07-2016

Thank you, it works for me. i would like to extract only Percent 20, 40 and 99.5 for quantile, How i can do that? ODS output FitQuantiles=Quantiles&i. Percent= 20 40 99.5;

Azeddine · ‎09-06-2016

Thank you, it works for me

Azeddine · ‎09-06-2016

Thank you very much

Azeddine · ‎09-06-2016

I would like to save All fit statistics table in a sas table. My code also available here is http://support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/viewer.htm#etsug_severity_sect029.htm /*--- Simulate a Normal sample affected by Regressors ---*/ data testnorm_reg(keep=y x1-x5 Sigma); array x{*} x1-x5; array b{6} _TEMPORARY_ (1 0.5 . 0.75 -2 1); call streaminit(34567); label y='Normal Response Influenced by Regressors'; do n = 1 to 100; /* simulate regressors */ do i = 1 to dim(x); x(i) = rand('UNIFORM'); end; /* make x2 linearly dependent on x1 and x3 */ x(2) = x(1) + 5 * x(3); /* compute log of the scale parameter */ logSigma = b(1); do i = 1 to dim(x); if (i ne 2) then logSigma = logSigma + b(i+1) * x(i); end; Sigma = exp(logSigma); y = rand('NORMAL', 25, Sigma); output; end; run; /*--- Set the search path for functions defined with PROC FCMP ---*/ options cmplib=(work.sevexmpl); /*-------- Fit models with PROC SEVERITY --------*/ proc severity data=testnorm_reg print=all plots=none; model y=x1-x5; dist Normal_s; dist burr; dist logn; dist pareto; dist weibull; run;

Azeddine · ‎09-05-2016

I would like to use a binomial distribution with this proc, Can i do that? I would like to use a constant, can i do that?

Azeddine · ‎09-05-2016

How to export the model selection in a data set?

Azeddine · ‎09-05-2016

I use Proc severity and proc univariate to estimate my parameters of continuous probability function. I don't understand why statistics of Kolmogorov Smirnov is different with the two proc. At the same time, statistics have the same definition in the documentation : http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_univariate_sect037.htm http://support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/viewer.htm#etsug_severity_sect022.htm Other statistics like Anderson Darling and Cramer-von Mises are the same.

Azeddine · ‎09-01-2016

hello, I use Proc univariate to estimate data parameters, I would like to export Quantiles for Gamma Distribution in a data sas, How i can do that ? %let LoiSeverite =Gamma; data Grave; input VAR1; datalines; 150100 15000 17000 200000 300000 run; proc univariate data=Grave; histogram / midpoints=3. to 32043. by 2912.727273 &LoiSeverite vaxis = axis1 name = 'MyHist'; axis1 label = (a=90 r=0); /*Loi Gamma*/ qqplot Var1/height = 1 &LoiSeverite(Theta=est sigma=est Alpha=est color=red); ppplot / &LoiSeverite(Theta=est sigma=est Alpha=est) ctext=blue; ods output Quantiles=GoodnessOfFit; run;

Azeddine · ‎07-13-2016

Hello, When i use Proc severity and proc univariate to estimate parameters, i have a difference between my 2 estimate parameters. How i can resolve this problem? I would like to have exactly the same parameter. Exemple : DATA Grave2; INPUT Var1; CARDS; 148234 17967 24066 20357 2409 93913 24879 75094 42631 50359 22077 63590 25720 57214 228177 105531 77630 62990 99790 477139 ; RUN; /*a)Estimate parameter with proc Univariate*/ rsubmit; proc univariate data=Grave2; var VAR1; histogram / gamma(theta=0); qqplot / gamma(theta=0 alpha=est Sigma=est); ppplot / gamma(theta=0 alpha=est Sigma=est); histogram / lognormal(theta=0); qqplot / lognormal(theta=0 zeta=est Sigma=est); ppplot / gamma(theta=0 alpha=est Sigma=est); histogram / weibull(theta=0); qqplot / weibull(theta=0 C=est Sigma=est); ppplot / weibull(theta=0 C=est Sigma=est); histogram / pareto(theta=0); qqplot / pareto(theta=0 alpha=est Sigma=est); ppplot / pareto(theta=0 alpha=est Sigma=est); run; endrsubmit; /*b) Estimate parameter with Proc severity*/ rsubmit; proc severity data=Grave2 crit=aic covout plots=none; /* call proc on view */loss VAR1; title "Statistique des sinistres et selection de la loi par critère AIC"; dist gamma logn weibull gpd /*Pareto donne des résultats trop differents entre severity et univariate*/; /*dist _predefined_; Preselection de lois utilisées en réassurance*/ run; endrsubmit; Regards,

Azeddine · ‎07-08-2016

Hello, I recently discover proc severity, and i would like to know if a similar proc exist for the discrete value. I want from this proc for a set of discrete value : -> Parameters for Poisson, Binomiale, Binomiale Négative distribution, -> Test of adequation (Chi 2 test,loglikelihood,..) -> qqplot/pp plot

Azeddine · ‎06-24-2016

I would like the hcpdm for severity only, i would a frequency equal to a constant 1.

Online Status	Offline
Date Last Visited	‎09-20-2016 06:50 AM

Re: Rand()

Re: Rand()

Rand()

Re: Proc univariate

Re: Proc univariate

Re: Proc severity

Re: Proc univariate/severity

Re: Proc severity

proc hpcdm

Proc severity

Re: Proc univariate/severity

Re: Proc univariate

Re: Proc severity/univariate

Proc severity/univariate

Re: Rand()

Re: Rand()

Rand()

Re: Proc univariate

Re: Proc univariate

Re: Proc severity

Re: Proc univariate/severity

Re: Proc severity

proc hpcdm

Proc severity

Proc univariate/severity

Proc univariate

Proc severity/univariate

Proc Severity and frequency

Re: PROC HCPDM