Statistical Inference Based On Kernel Distribution Function Estimators
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Statistical Inference Based On Kernel Distribution Function Estimators
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Author : Rizky Reza Fauzi
language : en
Publisher: Springer Nature
Release Date : 2023-05-31
Statistical Inference Based On Kernel Distribution Function Estimators written by Rizky Reza Fauzi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Mathematics categories.
This book presents a study of statistical inferences based on the kernel-type estimators of distribution functions. The inferences involve matters such as quantile estimation, nonparametric tests, and mean residual life expectation, to name just some. Convergence rates for the kernel estimators of density functions are slower than ordinary parametric estimators, which have root-n consistency. If the appropriate kernel function is used, the kernel estimators of the distribution functions recover the root-n consistency, and the inferences based on kernel distribution estimators have root-n consistency. Further, the kernel-type estimator produces smooth estimation results. The estimators based on the empirical distribution function have discrete distribution, and the normal approximation cannot be improved—that is, the validity of the Edgeworth expansion cannot be proved. If the support of the population density function is bounded, there is a boundary problem, namely the estimator does not have consistency near the boundary. The book also contains a study of the mean squared errors of the estimators and the Edgeworth expansion for quantile estimators.
Density Estimation For Statistics And Data Analysis
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Author : Bernard. W. Silverman
language : en
Publisher: Routledge
Release Date : 2018-02-19
Density Estimation For Statistics And Data Analysis written by Bernard. W. Silverman and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-19 with Mathematics categories.
Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician. The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text. Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.
Statistical Inference
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Author : Ayanendranath Basu
language : en
Publisher: CRC Press
Release Date : 2011-06-22
Statistical Inference written by Ayanendranath Basu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-22 with Computers categories.
In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Stati
Nonparametric Econometrics
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Author : Qi Li
language : en
Publisher: Princeton University Press
Release Date : 2007
Nonparametric Econometrics written by Qi Li and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Business & Economics categories.
This is a graduate textbook for econometricians and statisticians containing developments in the field. It emphasises nonparametric methods for real world problems containing the mix of discrete and continuous data found in many applications.
Nonparametric Statistics And Mixture Models A Festschrift In Honor Of Thomas P Hettmansperger
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Author : David Hunter
language : en
Publisher: World Scientific
Release Date : 2011-01-03
Nonparametric Statistics And Mixture Models A Festschrift In Honor Of Thomas P Hettmansperger written by David Hunter and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-03 with Mathematics categories.
This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.
Smart Grid Using Big Data Analytics
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Author : Robert C. Qiu
language : en
Publisher: John Wiley & Sons
Release Date : 2017-04-17
Smart Grid Using Big Data Analytics written by Robert C. Qiu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-17 with Technology & Engineering categories.
This book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.
Nonparametric Functional Estimation
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Author : B. L. S. Prakasa Rao
language : en
Publisher: Academic Press
Release Date : 2014-07-10
Nonparametric Functional Estimation written by B. L. S. Prakasa Rao and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-10 with Mathematics categories.
Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. The main emphasis throughout the book is on the discussion of several methods of estimation and on the study of their large sample properties. Chapters are devoted to topics on estimation of density and related functions, the application of density estimation to classification problems, and the different facets of estimation of distribution functions. Statisticians and students of statistics and engineering will find the text very useful.
Handbook Of Monte Carlo Methods
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Author : Dirk P. Kroese
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-06
Handbook Of Monte Carlo Methods written by Dirk P. Kroese and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-06 with Mathematics categories.
A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation. Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.
Statistical Inference For Discrete Time Stochastic Processes
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Author : M. B. Rajarshi
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-07-08
Statistical Inference For Discrete Time Stochastic Processes written by M. B. Rajarshi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-08 with Mathematics categories.
This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.
Hydrological Drought
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Author : Lena M. Tallaksen
language : en
Publisher: Elsevier
Release Date : 2023-09-17
Hydrological Drought written by Lena M. Tallaksen and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-17 with Science categories.
Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, Second Edition provides a comprehensive review of processes and estimation methods for streamflow and groundwater drought. It includes a qualitative conceptual understanding of drought features and processes, a detailed presentation of estimation methods and tools, practical examples and impacts relevant for operational practice.The drought phenomenon and its diversity across the world are illustrated using a global set of daily streamflow series, whereas regional and local aspects of drought are studied using a combination of hydrological time series and catchment information. Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, Second Edition concludes with human impacts, including climate change impacts on drought, drought forecasting and early warning and examples of procedures on how to manage water during drought. The majority of the examples are taken from regions where the rivers run most of the year, but not exclusively. The material presented ranges from well-established knowledge and analysing methods to recent developments in drought research. Its nature varies accordingly, from a more traditional textbook and clear overview to that of a research paper, which introduces recent approaches and methodologies for drought analysis. - Includes a number of innovative tools (self-guided tours, worked examples and software) to support both the understanding and teaching of different methods for evaluating drought severity, human impacts, ecological effects of drought and regional methods that enable estimation - Offers applications/demonstrations using a comprehensive database of streamflow and thematic data from a large number of national and international agencies, which illustrate how data are used when evaluating drought severity - Presents the state of the art of hydrological drought, including well established knowledge as well as recent developments in drought research