Titlebook: Generalized Linear Models With Examples in R; Peter K. Dunn,Gordon K.‘Smyth Textbook 2018 Springer Science+Business Media, LLC, part of Sp

Clinch

Chapter 5: Generalized Linear Models: Structure,stematic component of the .?is then considered in greater detail (Sect.?.). Having discussed the two components of the ., .s?are then formally defined (Sect.?.), and the important concept of the deviance function is introduced (Sect.?.). Finally, using a .?is compared to using a regression model after transforming the response (Sect.?.).

提名的名單

Chapter 7: Generalized Linear Models: Inference,ersion asymptotic results (the large sample asymptotics do not apply), which are discussed in Sect.?. where guidelines are presented for when these results hold. We then consider inference when . is unknown (Sect.?.), and include a discussion of using the different estimates of ..

debase

Chapter 8: Generalized Linear Models: Diagnostics,bout using each type of residual and the nomenclature of residuals are given in Sect.?.. We then discuss techniques to remedy or ameliorate any weaknesses in the models (Sect.?.), including the introduction of quasi-likelihood (Sect.?.). Finally, collinearity is discussed (Sect.?.).

縮短

顛簸地移動

Pseudoephedrine

模范

冥界三河

Chapter 11: Positive Continuous Data: Gamma and Inverse Gaussian GLMs,an be modelled. Modelling positive continuous data is introduced in Sect.?., then the two most common .s?for modelling positive continuous data are discussed: gamma distributions (Sect.?.) and inverse Gaussian distributions (Sect.?.). The use of link functions is then addressed (Sect.?.). Finally, estimation of . is considered in Sect.?..

使迷醉

不可比擬

Youth and the Threats to Socialismregression models are first reviewed (Sect.?.), then residuals, the main tools of diagnostic analysis, are defined (Sect.?.). We follow with a discussion of the leverage, a measure of the location of an observation relative to the average observation location (Sect.?.). The various diagnostic tools