Chapter 3 Notes

n e yi i is a function of i for a glm g i

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Unformatted text preview: i )b(θi ) + c (θi )d (yi ); i = 1, 2, . . . , n] E (Yi ) = µi is a function of θi . For a GLM, g (µi ) = xit β where xit = (xi 1 , xi 2 , . . . , xip ) set of covariates (predictors) for Yi . β t = (β1 , β2 , . . . , βp ) set of regression coefficients. g (·) is the link function. Monotone and differentiable. g (·) is a modeling choice. Linear regression: g (µi ) = µi . UNM Example 3.4 Y1 , Y2 , . . . , Yn are n-independent success-failure trials. P (Yi = 1) = π and P (Yi = 0) = 1 − π ., π is the probability of success. Probability function of Yi...
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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.

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