Beschreibung Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science). Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.
[PDF] predictive modeling applications in actuarial ~ Download Predictive Modeling Applications In Actuarial Science Volume 1 Predictive Modeling Techniques books, Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core .
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Predictive Modeling Applications in Actuarial Science ~ Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance (International Series on Actuarial Science) / Frees, Edward W., Meyers, Glenn, Derrig, Richard A. / ISBN: 9781107029880 / Kostenloser Versand für alle Bücher mit Versand und Verkauf duch .
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Applied Predictive Modeling / Max Kuhn / Springer ~ Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems.
What is predictive modeling? - Definition from WhatIs ~ Other more complex predictive models include decision trees, k-means clustering and Bayesian inference, to name just a few potential methods.. The most complex area of predictive modeling is the neural network.This type of machine learning model independently reviews large volumes of labeled data in search of correlations between variables in the data.
Generalized Linear Models for Insurance Data International ~ Generalized Linear Models for Insurance Data (International Series on Actuarial Science) / de Jong, Piet, Heller, Gillian Z. / ISBN: 9780521879149 / Kostenloser Versand für alle Bücher mit Versand und Verkauf duch .
Computational Actuarial Science with R Chapman & Hall/CRC ~ A Hands-On Approach to Understanding and Using Actuarial Models. Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to .
Universitat de Barcelona ~ [174] Guillen, M., Parner, J., Densgsoe, C. and Pérez-Marín, A. M. (2003) “Using logistic regression models to predict and understand why customer leave an insurance company” 465-490, chapter 13 in Intelligent Techniques in The Insurance Industry: Theory and Applications, edited by Lakhmi Jain and Arnold Shapiro, World Scientific.
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Monte Carlo method - Wikipedia ~ Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other .