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RE: st: Model for Poisson-shaped distribution but with non-count data


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Model for Poisson-shaped distribution but with non-count data
Date   Mon, 5 Dec 2011 21:32:30 -0500

Hi Owen,
It might help further if we knew exactly what your DV was.  I don't know about a transformation... what about robust standard errors and rescaled fit statistics?

Maas, C.J.M., & Hox, J.J. (2004a). The influence of violations of assumptions on multilevel parameter estimates and their standard errors. Computational Statistics & Data Analysis, 46, 427–440.http://igitur-archive.library.uu.nl/fss/2007-1004-200713/Maas(2004)_influence%20of%20violations.pdf

Maas, C.J.M., & Hox, J.J. (2004b). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58, 127–137.http://joophox.net/publist/sn04.pdf

Zhou, J., Zhu, H. (2003). Robust Estimation and Design Procedures for the Random Effects Model. The Canadian Journal of Statistics,  31(1), 99-110.

Yuan, K.-H., & Bentler, P.M. (2002). On normal theory based inference for multilevel models with distributional violations. Psychometrika, 67(4), 539-561.http://www.springerlink.com/content/833158k606754897/fulltext.pdf

Dedrick, R.F., Ferron, J.M., Hess, M.R., Hogarty, K.Y., Kromrey, J.D., Lang, T.R., Niles, J.D., & Lee, R.S. (2009). Multilevel Modeling: A Review of Methodological Issues and Applications. Review of Educational Research, 79(1), 69-102.

Best,
Cam

> Date: Mon, 5 Dec 2011 15:34:09 -0800
> Subject: st: Model for Poisson-shaped distribution but with non-count data
> From: ogallupe@gmail.com
> To: statalist@hsphsun2.harvard.edu
> 
> Hello,
> 
> Does anyone know what type of regression model I should use? I've been
> searching and have not been able to find a modeling approach designed
> to meet the distributional properties of a variable I am hoping to
> analyse.
> 
> The dependent variable has what looks like a Poisson distribution, but
> with non-count data. About 28% of the sample scores somewhere between
> 0 and 1. The highest value is 182.6. Skew = 2.256; kurtosis = 10.002.
> N=2776.
> 
> I have tried bootstrapped linear regressions and linear regressions
> after employing a normalizing transformation using lnskew0 (though the
> normalization is not perfect and results in a bimodal residual
> distribution).
> 
> One further complication is that I need to include random intercepts.
> 
> If anyone could help, it would be very  much appreciated.
> 
> Regards,
> 
> Owen Gallupe
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