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From |
<[email protected]> |

To |
[email protected] |

Subject |
st: d0 and Log-logistic hazard |

Date |
Tue, 27 Aug 2002 10:13:06 -0700 (PDT) |

I am trying to write a d0 maximum likelihood code for estimating a split-population duration model with gamma unobserved individual heterogeneity and log-logistic hazard. The estimation strategy finds first initial values from streg and set the initial probability of never fail to 2%. Then the ml code is used. I already checked that my log-likelihood is correctly written and the ml check is ok. But still Stata finds that the log-likelihood function is not concave and does not converges. Having this in mid, and in the spirit to find my potential mistake, I tried to create a d0 code for the standard duration model with gamma unobserved individual heterogeneity from lloghet_glf. Basically I did no changes to the code but take away the part that refers to d1 and d2 methods. Running this alternative program I learned that Stata does the same: it keeps saying that the log-likelihood function is not concave and it does not converges. Hence, it seems that the problem is not in the d0 code. Why is this is happening? Is it that a d1 (or maybe a d2) method is required whenever a log-logistic model is to be estimated? Could you give me some hint on what further action should I take? I sending to you a do file with the simplified version of the lloghet_glf code so that you can see what is my problem. Thank you, ALFONSO MIRANDA University of Warwick -----------------Do-File------------------------------ use http://www.stata-press.com/data/r7/cancer, clear set more off stset studytime, fail(died) streg drug age, d(logl) matrix B0 = e(b) matrix cr = -1 matrix colnames cr = ln_the:_cons matrix B1=(B0,cr) capture program drop loglgaspl_ll program define loglgaspl_ll version 7 args todo b lnf tempvar beta lngamma lntheta mleval `beta' = `b', eq(1) mleval `lngamma' = `b', eq(2) scalar mleval `lntheta' = `b', eq(3) scalar local t = _t local d = _d quietly { scalar `lngamma'=cond(`lngamma'<-20,-20,`lngamma') scalar `lntheta'=cond(`lntheta'<-20,-20,`lntheta') tempname th ga tempvar a b c scalar `ga' = exp(-`lngamma') /* gamma really 1/gamma */ scalar `th' = exp(`lntheta') gen double `a' = 1/`th'+`d' if $ML_samp gen double `b' = 1+(`t'*exp(-`beta'))^`ga' if $ML_samp gen double `c' = ln(1+`th'*ln(`b')) if $ML_samp mlsum `lnf' = -`a'*`c' + `d'*(`ga'*(ln(`t') - /* */ `beta') - `lngamma' - ln(`b')) if `todo'==0 | `lnf'==. { exit } } end ml model d0 loglgaspl_ll (_t: _t = drug age) (ln_gam:) (ln_the:) ml init B1 ml maximize, search(off) exit ----------------------------------------------------- __________________________________________________ Do You Yahoo!? Yahoo! Finance - Get real-time stock quotes http://finance.yahoo.com * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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