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

From   Cameron McIntosh <[email protected]>
To   STATA LIST <[email protected]>
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.

Maas, C.J.M., & Hox, J.J. (2004b). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58, 127–137.

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.

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.


> Date: Mon, 5 Dec 2011 15:34:09 -0800
> Subject: st: Model for Poisson-shaped distribution but with non-count data
> From: [email protected]
> To: [email protected]
> 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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