Results 251 to 260 of about 311,144 (289)

Generalized Additive Models

2010
This chapter returns to the problem of modelling the effect of continuous variables like age or engine power. Introducing the concept of penalized deviances leads to the use of cubic splines, a well-known tool in numerical analysis. Representing cubic splines in terms of so called B-splines makes it possible to formulate an estimation problem in terms ...
Esbjörn Ohlsson, Björn Johansson
openaire   +1 more source

Generalized Additive Models

2004
In Chapter 8 we discussed additive models (AM) of the form $$ E(Y|X) = c + \sum\limits_{\alpha = 1}^d {g_\alpha (x_\alpha )} . $$ (1) Note that we put EY = c and E(g α (X α ) = 0 for identification.
Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich   +3 more
openaire   +2 more sources

Generalized Additive Models

2001
The multiple linear regression model discussed in Chapter 8 and the generalized linear model covered in Chapters 9 and 10 accommodate nonlinear relationships between the response variable (or the link function of its mean) and one or more of the explanatory variables by using polynomial terms or parametric transformations. (The predictor remains linear
Brian Everitt, Sophia Rabe-Hesketh
openaire   +1 more source

Generalized Additive Models; Some Applications

Journal of the American Statistical Association, 1985
Abstract Generalized additive models have the form η(x) = α + σ fj (x j ), where η might be the regression function in a multiple regression or the logistic transformation of the posterior probability Pr(y = 1 | x) in a logistic regression. In fact, these models generalize the whole family of generalized linear models η(x) = β′x, where η(x) = g(μ(x ...
Trevor Hastie, Robert Tibshirani
openaire   +1 more source

Generalized Additive Models

2003
The models fit in Chap. 2 have two limitations. First, the conditional distribution of the response, given the predictors, is assumed to be Gaussian. Second, only a single predictor is allowed to have a smooth nonlinear effect—the other predictors are modeled linearly.
openaire   +1 more source

Generalized boosted additive models

2011
Regression analysis is a central method of statistical data analysis, but it is often inappropriate to model the relationship between the conditional distribution of a dependent variable as a function of one or more predictors when this relationship is characterized by complex nonlinear patterns. In such cases nonparametric regression methods are more
AMODIO, SONIA, J. J. Meulman
openaire   +3 more sources

Generalized additive models for functional data

TEST, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Febrero-Bande, Manuel, González-Manteiga, Wenceslao   +1 more
openaire   +1 more source

Generalized Additive Mixed Models

2020
In this chapter we introduce the Generalized Additive Model (GAM). GAMs enable the analyst to investigate non-linear functional relations between a response variable and one or more predictors. Furthermore, GAMs provide a principled framework for studying interactions involving two or more numeric predictors.
R. Harald Baayen, Maja Linke
openaire   +1 more source

Generalized additive models for medical research

Statistical Methods in Medical Research, 1995
This article reviews flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints. Applications to survival models and binary outcome models are illustrated.
T, Hastie, R, Tibshirani
openaire   +2 more sources

Generalized additive mixed models

Communications in Statistics - Theory and Methods, 2000
Following the extension from linear mixed models to additive mixed models, extension from generalized linear mixed models to generalized additive mixed models is made, Algorithms are developed to compute the MLE's of the nonlinear effects and the covariance structures based on the penalized marginal likelihood.
openaire   +1 more source