Generalized Linear Models: A Bayesian Perspective (Biostatistics (New York, N.Y.), 5.

[ŤêΧâŠ]

irst Sentence:
ABSTRACT Generalized linear models (GLMs) offer a unifying class of models which are widely used in regression analysis. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dynamic generalized linear models, posterior model probabilities, prior model probabilities, probit normal model, student retention data, determination using predictive distributions, ith historical study, exchangeable random effects, correlated ordinal data, proposal transition density, posterior ordinate, posterior density estimation, correlated binary data, reparameterization technique, binary regression models, polychotomous response data, small area effects, small area estimation, elicited ratings, variable subset selection, largest posterior probability, correlated random effects, multivariate probit model, item response model, generalized linear mixed models
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, New York, Journal of the Royal Statistical Society, Oxford University Press, Department of Statistics, John Wiley, Marcel Dekker, Statistical Science, Six Cities, The Annals of Statistics, Stochastic Search Variable Selection, Imperial College, University of Connecticut, References Albert, Repeat Steps, University of Arkansas, Statistica Sinica, Baton Rouge, Bayesian Estimates of the Mean-Ranking Measures, Biostatistics Unit, Cambridge University Press, Canadian Journal of Statistics, Cath Only, Duke University, Journal of Chemical Physics

şifre: hackhell

DownloadLink: http://rapidshare.com/files/64591337/Generalized_Linear_Models_-_A_Bayesian_Perspective.rar

 

[H@YDUT]

do you speak turkısh texas :durdurun

 

[belbar]

bad link please give true link