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Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is given to the implementation of presented methods in Matlab. All created programs are included in a special toolbox which is an integral part of the book. This toolbox contains many Matlab scripts useful for kernel smoothing of density, cumulative distribution function, regression function, hazard funct

Smoothing (Statistics) --- Kernel functions. --- Functions, Kernel --- Functions of complex variables --- Geometric function theory --- Curve fitting --- Graduation (Statistics) --- Roundoff errors --- Statistics

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Modeles mathematiques. --- Moindres carres --- Statistik. --- Methode der kleinsten Quadrate. --- Mathematical models. --- Least squares --- Method of least squares --- Squares, Least --- Curve fitting --- Geodesy --- Mathematical statistics --- Mathematics --- Probabilities --- Triangulation --- Models, Mathematical --- Simulation methods --- Informatique. --- Data processing. --- Least squares. --- Curve fitting. --- Fitting, Curve --- Numerical analysis --- Smoothing (Numerical analysis) --- Statistics --- Graphic methods

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Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

Bonds --- Bond issues --- Debentures --- Negotiable instruments --- Securities --- Debts, Public --- Stocks --- Mathematical models. --- Mathematical models --- E-books --- Finanzas. --- Bonos --- Especulación --- Modelos matemáticos. --- Bonds - Mathematical models --- AFNS. --- Bayesian analysis. --- DNS. --- NelsonГiegel curve fitting. --- RudebuschЗu model. --- affine arbitrage-free models. --- arbitrage-free NelsonГiegel models. --- arbitrage-free dynamic NelsonГiegel. --- arbitrage-free models. --- credit spreads. --- dynamic NelsonГiegel model. --- dynamic NelsonГiegel modeling. --- dynamic yield curve forecasting. --- dynamic yield curve modeling. --- factor loadings. --- forecasting. --- macro-finance yield curve modeling. --- multicountry modeling. --- risk management. --- stateгpace structure. --- stochastic volatility. --- yield curve fitting. --- yield curve models. --- yield curve.

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This book focuses on extraction of pertinent information from statistical test outputs, in order to write result sections and/or accompanying tables and/or figures. The book is divided into two encompassing sections: Part I – Basic Statistical Tests and Part II – Advanced Statistical Tests. Part I includes 9 basic statistical tests, and Part II includes 7 advanced statistical tests. Each chapter provides the name of a basic or advanced statistical test, a brief description, examples of when to use each, a sample scenario, and a sample results section write-up. Depending on the test and need, most chapters provide a table and/or figure to accompany the write-up. The purpose of the book is to provide researchers with a reference manual for writing results sections and tables/figures in scholarly works. The authors fill a gap in research support manuals by focusing on sample write-ups and tables/figures for given statistical tests. The book assists researchers by eliminating the need to comb through numerous publications to determine necessary information to report, as well as correct APA format to use, at the close of analyses.

Statistical hypothesis testing. --- Research --- Statistics --- Diagrams, Statistical --- Statistical diagrams --- Statistical tables --- Hypothesis testing (Statistics) --- Significance testing (Statistics) --- Statistical significance testing --- Testing statistical hypotheses --- Statistical methods. --- Graphic methods. --- Education. --- Education, general. --- Curve fitting --- Distribution (Probability theory) --- Hypothesis --- Mathematical statistics --- Children --- Education, Primitive --- Education of children --- Human resource development --- Instruction --- Pedagogy --- Schooling --- Students --- Youth --- Civilization --- Learning and scholarship --- Mental discipline --- Schools --- Teaching --- Training --- Education --- Tables.

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The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence All topics are completely demonstrated with the aid of the commercial computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source so the detailed code of every method is freely accessible. All examples and applications shown throughout the book may be used and customized by the reader without any restrictions.

Machine learning -- Periodicals. --- Machine learning. --- Computational intelligence --- Curve fitting --- Cluster analysis --- Engineering & Applied Sciences --- Computer Science --- Science --- Curve fitting. --- Computational intelligence. --- Statistical methods. --- Intelligence, Computational --- Learning, Machine --- Fitting, Curve --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial intelligence --- Soft computing --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Mathematics --- Numerical analysis --- Least squares --- Smoothing (Numerical analysis) --- Statistics --- Graphic methods --- Artificial Intelligence. --- Mathematical and Computational Engineering.