Poisson Regression Notes

I the model is yi poissoni i i i i expxt i 1

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Xi 1 + β2 Xi 2 + . . . + βp Xip ) = exp(xt β ). i The model is Yi ∼ Poisson(λi ); λi = ηi θi = ηi exp(xt β ), i = 1, 2, . . . , n i In log-scale, log (λi ) = log (ηi ) + β1 Xi 1 + β2 Xi 2 + . . . + βp Xip = offset + linear predictor UNM For a covariate Xj , factor is absent Xj = 0 and factor present if Xj = 1. The Rate ratio (RR) RR = E (Yi |present) η exp(β0 + β1 ) =i = exp(β1 ) E (Yi |absent) ηi exp(β0 ) If a covariate is increased by one unit, exp(β1 ) is the effect due to the increase. ˆ In general, the RR for cov...
View Full Document

This note was uploaded on 01/27/2014 for the course STAT 574 taught by Professor Gabrielhuerta during the Fall '13 term at New Mexico.

Ask a homework question - tutors are online