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Author: Sevda Alaca
Publisher: GRIN Verlag
ISBN: 3668478635
Category : Mathematics
Languages : en
Pages : 86
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Book Description
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 neural networks. 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 are only a few of them.
Author: Sevda Alaca
Publisher: GRIN Verlag
ISBN: 3668478635
Category : Mathematics
Languages : en
Pages : 86
Get Book Here
Book Description
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 neural networks. 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 are only a few of them.
Author: Stéphane Vannitsem
Publisher: Elsevier
ISBN: 012812248X
Category : Science
Languages : en
Pages : 364
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Book Description
Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
Author: Jorge M. Uribe
Publisher: Springer Nature
ISBN: 3030445046
Category : Business & Economics
Languages : en
Pages : 63
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Book Description
This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.
Author: Dilanka Shenal Dedduwakumara
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 260
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Book Description
Statistical modeling and estimation of quantiles is an integral part of statistical data analysis in interpreting real-world phenomena. The purpose of this thesis is to contribute to the existing body of knowledge in quantile-based methods in modeling and estimation while providing simulation studies and real data applications supporting the new contributions. The results are discussed coherently in the thesis, including original publications that have been either published, accepted to be published or submitted for peer review. In the first part of the thesis, we propose a new approach based on the Probability Density Quantile (pdQ) for parameter estimation of the Generalized Lambda Distribution (GLD). Defined in terms of a location parameter, scale parameter, and two shape parameters, the GLD is widely used for modeling in many fields because of its flexibility in being able to mimic many other distributions. However, due to there being four parameters, choosing optimal parameters and/or estimating those parameters is not straightforward. We compare our pdQ approach with the existing methods in regards to time efficiency and performance. Further, we extend the introduced method for the Generalized Beta Distribution, illustrating the applicability of the method more broadly than just the GLD. In the second part of the thesis, we introduce several methods, including a method based on the GLD, to obtain confidence intervals for quantiles when only a frequency distribution or histogram is available. These methods are extended to measuring inequality for grouped income data where data is often provided in such summary format to protect the confidentiality of individuals. Here we show that interval estimators for quantile-based inequality measures are suited to this type of data. The thesis also includes two web-based Shiny Applications for end-users to apply these methods in their research.
Author: Jakóbczak, Dariusz Jacek
Publisher: IGI Global
ISBN: 1466694599
Category : Business & Economics
Languages : en
Pages : 466
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Book Description
Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.
Author: Cesare Alippi
Publisher: Springer
ISBN: 3642042775
Category : Computers
Languages : en
Pages : 1034
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Book Description
This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14–17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.
Author: Alvin S. Goodman
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 34
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Book Description
The large random access memory and high internal speeds of present day computers can be used to increase the efficiency of large-scale simulation experiments by estimating simultaneously several quantiles of each of several statistics. In order to do this without inordinately increasing programming complexity, quantile estimation schemes are required which are simple and do not depend on special features of the distributions of the statistics considered. The author discusses limitations, when the probability level alpha is very high or very low, of two basic methods of estimating quantiles. One method is the direct use of order statistics; the other is based on the use of stochastic approximation. Several modifications of these two estimation schemes are considered. In particular a simple and computationally efficient transformation of the simulation data is proposed and the properties (i.e. bias and variance) of quantile estimates based on this scheme are discussed. (Author).
Author: European Coordinating Committee for Artificial Intelligence
Publisher: IOS Press
ISBN: 160750605X
Category : Computers
Languages : en
Pages : 1184
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Book Description
LC copy bound in 2 v.: v. 1, p. 1-509; v. 2, p. [509]-1153.
Author: James J. Park
Publisher: Springer
ISBN: 9811313288
Category : Technology & Engineering
Languages : en
Pages : 840
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Book Description
This book presents the combined proceedings of the 12th International Conference on Multimedia and Ubiquitous Engineering (MUE 2018) and the 13th International Conference on Future Information Technology (Future Tech 2018), both held in Salerno, Italy, April 23 - 25, 2018. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing.
Author: David E. Rapach
Publisher: Emerald Group Publishing
ISBN: 1849505403
Category : Business & Economics
Languages : en
Pages : 691
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Book Description
Forecasting in the presence of structural breaks and model uncertainty are active areas of research with implications for practical problems in forecasting. This book addresses forecasting variables from both Macroeconomics and Finance, and considers various methods of dealing with model instability and model uncertainty when forming forecasts.
