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Kernel Methods and Estimation of Extreme Value Index

estimation of extreme value index (EVI). We apply both the parametric approach and the DOA approach to estimate EVI and the extreme quantile. We;

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Modeling Energy Demand using Nonparametric and Extreme Value Theory

kernel estimation methods and extreme value theory are discussed and the asymptotic properties of the estimators given. The methods are applied;

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Modeling of extreme events and stress testing analysis

scenarios. The methodology is applied to S&P 500 Index and to dollar / euro exchange rate. Additionally, the estimation of a generalized extreme;

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Extreme Value Methods with Applications to Finance

, Poisson cluster approximation, and nonparametric estimation methods. These topics have not been fully focused on in other books on extremes;

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Asymmetric Kernel Smoothing

kernels are compared with those smoothed by symmetric kernels. Lastly, the book addresses the applications of asymmetric kernel estimation and;

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Kernel Smoothing

estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods;

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Density Estimation for Statistics and Data Analysis

. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data;

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Quantify!

Goran Grimvall is determined to help mere mortals understand how scientists get to the kernel of perplexing problems. Entertaining and;

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Pioneering Works on Extreme Value Theory

empirical beta copula, and regression analysis on imbalanced binary data, as well as the semiparametric estimation of the upper bound of extrema;

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Kernel Smoothing

, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and;

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Quantify! - A Crash Course in Smart Thinking

Goran Grimvall is determined to help mere mortals understand how scientists get to the kernel of perplexing problems. Entertaining and;

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Applied Nonparametric Econometrics

the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and;

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Applied Nonparametric Econometrics

the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and;

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Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems

Rare event probability (10-4 and less) estimation has become a large area of research in the reliability engineering and system safety;

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Extreme Value Theory with Applications to Natural Hazards

estimation techniques based on both decision-theoretic features (e.g., Bayesian theory), empirical robustness and a valid treatment of uncertainties;

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Applying Test Equating Methods

conducted with the program R. The book covers traditional equating methods including, mean and linear equating, frequency estimation equating and;

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Handbook of Monte Carlo Methods

* Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation;

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Statistical Analysis of Extreme Values

An Overview of Reduced-Bias Estimation; The Spectral Decomposition Methodology; About Tail Independence; and Extreme Value Statistics;

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Functional Estimation For Density, Regression Models And Processes

presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single;

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Kernel Methods for Remote Sensing Data Analysis

Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have;

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Levy Matters IV

biosciences, and telecommunication. The three chapters of this volume are completely dedicated to the estimation of Levy processes, and are;

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Nonparametric Methods in Statistics with SAS Applications

curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics;

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Nonparametric Methods In Statistics With Sas Applications

curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics;

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Error Estimates for Advanced Galerkin Methods

This monograph provides a compendium of established and novel error estimation procedures applied in the field of Computational Mechanics;

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Digital Signal Processing with Kernel Methods

communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing;

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Empirical Risk Modeling of Financial Time Series using Value at Risk

Extreme Value Theory (EVT) and the Generalized Pareto Distribution (GPD). The quality of the risk estimation approach with its corresponding;

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Kernel Mean Embedding of Distributions

and statistical inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS;

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