Results 231 to 240 of about 124,550 (275)
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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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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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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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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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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
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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
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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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ACM SIGSIM Simulation Digest, 1973
The following are some additional generator routines to supplement those published by Robert E. Wheeler. The first is the (usual?) straightforward routine for the geometric distribution. The second is for the generation of the highest (or lowest) variates in ordered samples (from the uniform distribution).
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The following are some additional generator routines to supplement those published by Robert E. Wheeler. The first is the (usual?) straightforward routine for the geometric distribution. The second is for the generation of the highest (or lowest) variates in ordered samples (from the uniform distribution).
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, 2000