Results 111 to 120 of about 62,879 (156)
Impact of Influenza on Outpatient Visits, Hospitalizations, and Deaths by Using a Time Series Poisson Generalized Additive Model. [PDF]
PLoS One, 2016 Guo RN +10 more
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Enhancing clinical insights in glioma grading using Bayesian Optimization and Explainable AI. [PDF]
J Genet Eng BiotechnolElsayad AM, Elsayad OA.
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Web-Based Application of Simplified Machine Learning for Detecting Reduced LVEF From 12-Lead ECG. [PDF]
J ArrhythmKawakami H +6 more
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Nonlinear effects of ambient temperature and treatment days on nocturnal enuresis using generalized additive models. [PDF]
Sci RepMiyano T +4 more
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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
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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
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
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
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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
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
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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
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
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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
openaire +1 more source