Machine Learning in Insurance

Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand...

Повний опис

Збережено в:

Бібліографічні деталі
Формат:	Online
Мова:	Англійська
Опубліковано:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Предмети:	deposit insurance implied volatility static arbitrage parameterization machine learning calibration dichotomous response predictive model tree boosting GLM validation generalised linear modelling zero-inflated poisson model telematics benchmark cross-validation prediction stock return volatility long-term forecasts overlapping returns autocorrelation chain ladder Bornhuetter–Ferguson maximum likelihood exponential families canonical parameters prior knowledge accelerated failure time model chain-ladder method local linear kernel estimation non-life reserving operational time zero-inflation overdispersion automobile insurance risk classification risk selection least-squares monte carlo method proxy modeling life insurance Solvency II claims prediction export credit insurance semiparametric modeling VaR estimation analyzing financial data n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Онлайн доступ:	ONIX_20210501_9783039364473_487
Теги:	Додати тег Немає тегів, Будьте першим, хто поставить тег для цього запису!

Machine Learning in Insurance

Схожі ресурси: Machine Learning in Insurance

Схожі ресурси