This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models that have been developed on the basis of GLM theory, including GAM, ordered binomial models, multinomial logit and probit models, GEE and other quasi-likelihood models, fixed and random effects models, and random intercept and ...
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This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models that have been developed on the basis of GLM theory, including GAM, ordered binomial models, multinomial logit and probit models, GEE and other quasi-likelihood models, fixed and random effects models, and random intercept and random parameter models. Using Stata, the book offers numerous examples to assist you in applying the models to your own data situations.
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Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
