Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Steve Samuels <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Truncated at zero count data with underdispersion |

Date |
Mon, 11 Oct 2010 20:07:26 -0400 |

"What do you think about a glm log gamma distribution?" I don't think much of it. Your data are discrete and bounded. Steve [email protected] On Mon, Oct 11, 2010 at 4:44 PM, Laurie Molina <[email protected]> wrote: > Thank you very much, i will work on your suggestion. > I just would like to ask for some comments on the following: > What do you think about a glm log gamma distribution? > With the log link i ensure that the conditional expectation is > positive, and i know i lose the posibility of predicting puntual > probabilities, but with the log gamma i can have underdispersion with > consistency, isnt it? > > Thank you again! > > > > > > On Mon, Oct 11, 2010 at 1:36 PM, Steve Samuels <[email protected]> wrote: >> "> Does anyone know any stata command that i could use to model zero >>> truncated count data with underdispersion?" >> >> There are too possibilities: >> 1) Your model is inadequate >> or >> 2) The Poisson distribution doesn't fit your data-my best guess. >> >> If the Poisson model doesn't fit, use -mlogit- or -ologit-, with >> categories being the numbers of cell phones. You might have to >> combine sparse categories. Since your goal is prediction in an >> external data set, split the study data set into two parts; develop >> the model on one part, and assess the predictive accuracy of the model >> on the second. (There are probably also -jackknife- or -boostrap- >> possibilities for getting cross-validated "honest" assessments of >> accuracy.) >> >> Here's an example of assessing predictive accuracy from -mlogit-. The >> predicted category is that with the highest probability, and >> predictive criterion is the difference between observed and predicted >> category and its root MSE. >> >> ***********CODE BEGINS************* >> sysuse auto, clear >> recode rep78 1/2 = 2 >> mlogit rep78 mpg trunk >> forvalues i = 2/5{ >> predict p`i', outcome(`i') >> } >> egen pmax = rowmax(p2 p3 p4 p5) >> >> gen p_class = 2 >> forvalues i =3/5{ >> replace p_class = `i' if pmax ==p`i' >> } >> label var p_class "Predicted Category" >> >> gen diff = rep78 - p_class >> tab diff >> sum diff >> scalar mse = r(var) + r(mean)^2 >> di mse >> >> ***********CODE ENDS************** >> >> >> >> >> On Sun, Oct 10, 2010 at 6:39 PM, Laurie Molina <[email protected]> wrote: >>> Hi all, >>> >>> I have a question, i hope somebody can help my. >>> >>> I am modelling count data truncated at zero: The number of cell phones >>> of households with cells phone. The observed data goes from 1 to 9, >>> with mean equals 1.89 and variance 1.14. >>> I have done some underdispersion tests after running a poisson >>> regression with the truncated data and i reject the one sided >>> hypothesis of equidispersion with a p-value of cero. (the predicted >>> values have mean 1.89 with variance equal .51). >>> >>> Regarding the latent variable, i have also availabre the number of >>> cellphones of all the households, i.e. i have the data of the latent >>> variable that goes from 0 to 9. Here the mean equals 1.15 and the >>> variance equals 1.54. I have also done and underdispersion test after >>> running a poisson regresion with all the data and i get >>> underdispersion (the predicted values have mean 1.15 but variance >>> equal .999). >>> >>> I am interested in the truncated regresion because i want to predict >>> the number of cell phones of the HH who have cell phones. I mean, in >>> addition to the data that i am using for this regresion, i have >>> another list of households and i know wheter they have or they dont >>> have a cell phone. But among the HH in that list, who do have a cell >>> phone, i do not know how many of them they have, and i am interested >>> in that. >>> >>> To my understand if i use a poisson regression, given that my data is >>> truncated i will get inconsistent estimates because the conditional >>> expectation will not be correctly specified as an exponential function >>> of xbeta. >>> So i have to use the command: >>> **** >>> ztp depvar indep var >>> **** >>> But if the latent variable is not poisson i will get inconsistent estimates. >>> >>> I know stata has also availabre the zero truncated negative binomial >>> regression, but since i get underdispersion in the latent variable i >>> think the data is not negative binomial distributed so i will still >>> get inconsistent estimates. >>> >>> Does anyone know any stata command that i could use to model zero >>> truncated count data with underdispersion? >>> >>> Thank you all very much in advance. >>> >>> Regards, >>> >>> Laurie. >>> * >>> * For searches and help try: >>> * http://www.stata.com/help.cgi?search >>> * http://www.stata.com/support/statalist/faq >>> * http://www.ats.ucla.edu/stat/stata/ >>> >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Truncated at zero count data with underdispersion***From:*Laurie Molina <[email protected]>

**Re: st: Truncated at zero count data with underdispersion***From:*Steve Samuels <[email protected]>

**Re: st: Truncated at zero count data with underdispersion***From:*Laurie Molina <[email protected]>

- Prev by Date:
**st: Error message** - Next by Date:
**st: Question on results export** - Previous by thread:
**Re: st: Truncated at zero count data with underdispersion** - Next by thread:
**Re: st: Truncated at zero count data with underdispersion** - Index(es):