Estimation of quantiles in a simulation model based on artificial neural networks
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Master's Thesis from the year 2017 in the subject Mathematics - Stochastics, grade: 1,3, Technical University of Darmstadt, language: English, abstract: This thesis deals with the development of an "alpha"-quantile estimate based on a surrogate model with the use of artificial neuralnetworks. Using artificial neural networks as an estimate is considered a nonparametric approach.The estimation of a specific quantile of a data population is a widely used statistical task and a comprehensive way to discover the true relationship among variables. It can be classified as nonparametric regression, w...
Master's Thesis from the year 2017 in the subject Mathematics - Stochastics, grade: 1,3, Technical University of Darmstadt, language: English, abstract: This thesis deals with the development of an "alpha"-quantile estimate based on a surrogate model with the use of artificial neuralnetworks. Using artificial neural networks as an estimate is considered a nonparametric approach.The estimation of a specific quantile of a data population is a widely used statistical task and a comprehensive way to discover the true relationship among variables. It can be classified as nonparametric regression, where it is one of the standard tasks. The most common selected levels for estimation are the first, second and third quartile (25, 50 and 75 percent). The quantile level is given by "alpha". A 25 percent quantile for example has 25 percent of the data distribution below the named quantile and 75 percent of the data distribution above it. Sometimes the tail regions of a population characteristic are of interest rather than the core of the distribution.Quantile estimation is applied in many different contexts - financial economics, survival analysis and environmental modelling areonly a few of them.
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