Bayesian and Classical Analysis of Poisson Regression

By G. M. El‐Sayyad. 1973

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Assumed Density Filtering Methods for Learning Bayesian Neural Networks (2016, methodology): “Here, we use an accurate approximation which induces an exponential family posterior predictive distribution (Chan and Vasconcelos 2009; El-Sayyad 1973).”
Energy-using appliances and energy-saving features: Determinants of ownership in Ireland (2008, methodology): “For an overview of Poisson models, see Wooldridge (2002) or El Sayyad (1973).”
Counting People With Low-Level Features and Bayesian Regression (2012, methodology): “In this paper, we extend [35] in three ways: 1) we derive a Gaussian posterior that can handle observations of zero count; 2) we derive a closed-form predictive count distribution; and 3) we kernelize the regression function, thus modeling nonlinear log-arrival rates.”
Econometric Analysis of Cross Section and Panel Data, 2nd Edition (2001, background): “; xKÞ has a Poisson distribution, as in El Sayyad (1973) and Maddala (1983, Section 2.”
Bayesian Poisson regression for crowd counting (2009, methodology): “(29) Using (27), this can be approximated as [10]”