Results 231 to 240 of about 124,195 (277)
Some of the next articles are maybe not open access.
Technometrics, 1992
Generalized Additive Models. By T. J. Hastie and R. J. Tibshirani. ISBN 0 412 34390. Chapman and Hall, London, 1990. 336 pp. £25.00.
Richard D. de Veaux +2 more
+4 more sources
Generalized Additive Models. By T. J. Hastie and R. J. Tibshirani. ISBN 0 412 34390. Chapman and Hall, London, 1990. 336 pp. £25.00.
Richard D. de Veaux +2 more
+4 more sources
International Journal of Game Theory, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
CESARI, GIULIA +2 more
openaire +2 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
CESARI, GIULIA +2 more
openaire +2 more sources
Aggregation Operators and Additive Generators
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2001Generated aggregation operators on continuous and discrete scales derived by means of a generating system are introduced. Linear generating systems are shown to be equivalent with the class of weighted means. The following classes are characterized: symmetric generated aggregation operators, idempotent generated aggregation operators, generated ...
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
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
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
openaire +2 more sources
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
openaire +2 more sources
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
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
2016
We present the basic theorems for cost minimization and for DPs with an absorbing set of states. We also prove the basic theorem using reachable states. The important notion of a bounding function is introduced.
Karl Hinderer +2 more
openaire +1 more source
We present the basic theorems for cost minimization and for DPs with an absorbing set of states. We also prove the basic theorem using reachable states. The important notion of a bounding function is introduced.
Karl Hinderer +2 more
openaire +1 more source
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
openaire +1 more source
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
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