Results 241 to 250 of about 281,620 (281)
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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 +3 more
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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 +3 more
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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
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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
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2016
This chapter formulates and demonstrates generalized additive models (GAMs) for means of continuous outcomes treated as independent and normally distributed with constant variances as in linear regression and for logits (log odds) of means of dichotomous discrete outcomes with unit dispersions as in logistic regression.
George J. Knafl, Kai Ding
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This chapter formulates and demonstrates generalized additive models (GAMs) for means of continuous outcomes treated as independent and normally distributed with constant variances as in linear regression and for logits (log odds) of means of dichotomous discrete outcomes with unit dispersions as in logistic regression.
George J. Knafl, Kai Ding
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Feature significance in generalized additive models
Statistics and Computing, 2007This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete.
B. Ganguli, Matt P. Wand
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Sparse Additive Generative Models of Text [PDF]
, 2018 Generative models of text typically associate a multinomial with every class label or topic. Even in simple models this requires the estimation of thousands of parameters; in multifaceted latent variable models, standard approaches require additional latent ``switching'' variables for every token, complicating inference.
Jacob Eisenstein +2 more
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Generalized additive models for functional data
TEST, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Febrero-Bande, Manuel +1 more
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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
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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
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On A General Fuzzy Additive Clustering Model
Intelligent Automation & Soft Computing, 1995ABSTRACTThe purpose of this article is to define a generalized structural model of similarity between a pair of objects. Applying a classification of a given data set as a structural model, we have developed an additive fuzzy clustering model.7,8 The essential merits of the additive fuzzy clustering models are 1) the amount of computations for the ...
Mika Sato, Yoshiharu Sato
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Generalized Additive Models; Some Applications
Journal of the American Statistical Association, 1985Abstract 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
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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.
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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.
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