Titlebook: Effective Statistical Learning Methods for Actuaries III; Neural Networks and Michel Denuit,Donatien Hainaut,Julien Trufin Textbook 2019 S

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书目名称Effective Statistical Learning Methods for Actuaries III影响因子(影响力)




书目名称Effective Statistical Learning Methods for Actuaries III影响因子(影响力)学科排名




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书目名称Effective Statistical Learning Methods for Actuaries III网络公开度学科排名




书目名称Effective Statistical Learning Methods for Actuaries III被引频次




书目名称Effective Statistical Learning Methods for Actuaries III被引频次学科排名




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书目名称Effective Statistical Learning Methods for Actuaries III年度引用学科排名




书目名称Effective Statistical Learning Methods for Actuaries III读者反馈




书目名称Effective Statistical Learning Methods for Actuaries III读者反馈学科排名

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Effective Statistical Learning Methods for Actuaries IIINeural Networks and

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Matthäus Ebinal,Valery Mitjuschkin Shu and Burn (Water Resour Res 40:1–10, 2004) forecast flood frequencies with an ensemble of networks. We start this chapter by describing the bias-variance decomposition of the prediction error. Next, we discuss how aggregated models and randomized models reduce the prediction error by decreasing

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Textbook 2019hods for Actuaries.. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently..

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Dimension-Reduction with Forward Neural Nets Applied to Mortality,native to principal component analysis (PCA) or non-linear PCA. In actuarial sciences, these networks can be used for understanding the evolution of longevity during the last century. We also introduce in this chapter a genetic algorithm for calibrating the neural networks. This method combined with a gradient descent speeds up the calibration.

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Neues Selbstbild und Rollenprofilnt of our a priori knowledge about parameters based on Markov Chain Monte Carlo methods. In order to explain those methods that are based on simulations, we need to review the main features of Markov chains.

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Lando Kirchmair,Daniel-Erasmus Khands of regularization for avoiding the overfitting. We next explain why deep neural networks outperform shallow networks for approximating hierarchical binary functions. This chapter is concluded by a numerical illustration.