Yield Curve Estimation by Kernel Smoothing Methods PDF Download
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Author: Oliver B. Linton
Publisher:
ISBN:
Category : Government securities
Languages : en
Pages : 42
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Book Description
Author: Oliver B. Linton
Publisher:
ISBN:
Category : Government securities
Languages : en
Pages : 42
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Book Description
Author: Oliver Linton
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 0
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Book Description
Author: O. Linton
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
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Book Description
Author: Sucharita Ghosh
Publisher: John Wiley & Sons
ISBN: 111845605X
Category : Mathematics
Languages : en
Pages : 272
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Book Description
Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as well as non-Gaussian data that are transformations of latent Gaussian processes. These types of data occur in many fields of research, e.g. the natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and surface estimation in particular for time series and spatial data and other topics such as rapid change points, robustness etc. are introduced alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples—making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. A simple and analytical description of kernel smoothing methods in various contexts Presents the basics as well as new developments Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers.
Author: T. Gasser
Publisher: Springer
ISBN: 3540384758
Category : Mathematics
Languages : en
Pages : 254
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Book Description
Author: M.P. Wand
Publisher: CRC Press
ISBN: 1482216124
Category : Mathematics
Languages : en
Pages : 227
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Book Description
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilita
Author: Masayuki Hirukawa
Publisher: Springer
ISBN: 9811054665
Category : Business & Economics
Languages : en
Pages : 117
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Book Description
This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics addressed include the bias-variance tradeoff, smoothing parameter choices, achieving rate improvements with bias reduction techniques, and estimation with weakly dependent data. Further, the large- and finite-sample properties of estimators and test statistics smoothed by asymmetric kernels are compared with those smoothed by symmetric kernels. Lastly, the book addresses the applications of asymmetric kernel estimation and testing to various forms of nonnegative economic and financial data. Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support. Yet many types of economic and financial data are nonnegative and violate the presumed conditions of conventional methods. Examples include incomes, wages, short-term interest rates, and insurance claims. Such observations are often concentrated near the boundary and have long tails with sparse data. Smoothing with asymmetric kernel functions has increasingly gained attention, because the approach successfully addresses the issues arising from distributions that have natural boundaries at the origin and heavy positive skewness. Offering an overview of recently developed kernel methods, complemented by intuitive explanations and mathematical proofs, this book is highly recommended to all readers seeking an in-depth and up-to-date guide to nonparametric estimation methods employing asymmetric kernel smoothing.
Author: M.P. Wand
Publisher: CRC Press
ISBN: 9780412552700
Category : Mathematics
Languages : en
Pages : 230
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Book Description
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernal Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.
Author: Ivanka Horova
Publisher: World Scientific
ISBN: 9814405493
Category : Mathematics
Languages : en
Pages : 242
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Book Description
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 function, indices of quality and bivariate density. Specifically, methods for choosing a choice of the optimal bandwidth and a special procedure for simultaneous choice of the bandwidth, the kernel and its order are implemented. The toolbox is divided into six parts according to the chapters of the book.All scripts are included in a user interface and it is easy to manipulate with this interface. Each chapter of the book contains a detailed help for the related part of the toolbox too. This book is intended for newcomers to the field of smoothing techniques and would also be appropriate for a wide audience: advanced graduate, PhD students and researchers from both the statistical science and interface disciplines.
Author: Sattar Mansi
Publisher:
ISBN:
Category :
Languages : en
Pages : 28
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Book Description
This paper surveys methodologies on the statistical approach to term structure estimation, also known as yield curve smoothing models. Specifically, term structure estimation methods are reviewed to determine the effects of the assumed functional form of the interpolating function and whether the methods' primary assumptions and estimation technique focus on the spot rate function, forward rate function, or discount function. To this end, we discuss the estimation of spot rates from on-the-run Treasuries, the estimation of spot rates, forward rates, and discount factors from all Treasuries, and the estimation of discount factors from Treasury STRIPS. The central papers under each section are described and their results are summarized. Different methodologies on the use of Treasury data are also discussed. Suggestions for future research are provided.
