Generalized linear models
Generalized Linear Models
... Special case: Overdispersion For the computation of GLM the dispersion parame- ter φ for the binomial and Poisson distribution is set equal to one. However, there exist cases of data dis- tribution which violate this ...
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Generalized Linear Models
... Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally ...of models where the response variable is discrete ...
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The OSCAR for Generalized Linear Models
... in both cases. A combination of the ridge and the LASSO uses λ 1 P p j=1 β j 2 +λ 2 P p j=1 |β j |. It is well known as the elastic net (Zou and Hastie, 2005). Zou and Hastie (2005) showed that variable selection leads ...
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Generalized Linear Models in Vehicle Insurance
... KAFKOVÁ SILVIE, KŘIVÁNKOVÁ LENKA. 2014. Generalized Linear Models in Vehicle Insurance. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 62(2): 383–388. Actuaries in insurance ...
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15.1 The Structure of Generalized Linear Models
... for Generalized Linear Models Most of the diagnostics for linear models presented in Chapters 11 and 12 extend relatively straightforwardly to ...of linear least-squares ...
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Fitting Models of Vulnerability to Toxicity with Generalized Linear Models
... results from his/her prolonged exposure to toxic substances. The individual will be ‘pronounced’ toxic, with respect to the toxic substance, if the estimated quantity of the substance found in the samples (e.g. blood or ...
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Linear hypothesis testing for high dimensional generalized linear models
... testing linear hypotheses in high- dimensional generalized linear ...with linear hypothe- ses, we first propose constrained partial regularization method and study its statistical ...and ...
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Ridge regression and diagnostics in generalized linear models
... ABSTRACT The first part of this thesis is concerned with the collinearity problem and ridge regression methodology in generalized linear models (GLMs). It is shown that collinearity among the ...
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Semiparametric Generalized Linear Models with the gldrm Package
... Semiparametric Generalized Linear Models with the gldrm Package by Michael J. Wurm and Paul J. Rathouz Abstract This paper introduces a new algorithm to estimate and perform inferences on a recently ...
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Tests for High Dimensional Generalized Linear Models
... There have been works on the post-variable selection inference associated LASSO and other variable selection methods for the linear models as in Berk et al. (2013) , Lee et al. (2014), Taylor et al. (2014), ...
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Tests for High Dimensional Generalized Linear Models
... There have been works on the post-variable selection inference associated LASSO and other variable selection methods for the linear models as in Berk et al. (2013) , Lee et al. (2014), Taylor et al. (2014), ...
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Bayesian Inference for Sparse Generalized Linear Models
... Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the ex- pectation propagation (EP) technique. The parameters can ...
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Exploiting regularity in sparse Generalized Linear Models
... Abstract— Generalized Linear Models (GLM) are a wide class of regression and classification models, where the predicted variable is obtained from a linear combination of the in- put ...
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Pricing of Car Insurance with Generalized Linear Models
... analysis. Generalized linear models can be a great way to efficiently predict important ratios, like the claim frequency, claim severity and pure ...
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Generalized Linear Models
... Assessing Model Fit V I Researchers also tried to establish measures that contain comparable information as the R 2 - measure in Linear Regression. These ’Pseudo’-R 2 measures can be useful for model comparison, ...
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Generalized linear models
... As Birch (1963) has shown, the estimation of a set of independent multinomial distributions is equivalent to the estimation of a set of independent Poisson distributions, and in[r] ...
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Semiparametric generalized linear models
... AHEAD Data: log-linear and proportional odds models for number of IADL difficulties. • Log-linear model:[r] ...
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A Practitioner's Guide to Generalized Linear Models
... As models fitted to only a single year of data could be distorted by events that occurred during that year, the data should ideally be based on two or three years of ...
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Generalized Linear Models for Insurance Data
... the generalized inverse Gaussian and inverse Gaussian ...The generalized inverse Gaussian is a three-parameter distribution which is highly flexible, but has the drawback that its computation is ...
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L2 differentiability of generalized linear models
... In this paper, we explicate the respective conditions (i)–(iii) for the cases of stochastic and deterministic regressors, respectively, in examples including – for reference and comparison – linear regression, ...
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