Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series
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Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series
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Author : K. Dzhaparidze
language : en
Publisher:
Release Date : 1986
Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series written by K. Dzhaparidze and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with categories.
Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series
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Author : K. Dzhaparidze
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series written by K. Dzhaparidze 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 2012-12-06 with Mathematics categories.
. . ) (under the assumption that the spectral density exists). For this reason, a vast amount of periodical and monographic literature is devoted to the nonparametric statistical problem of estimating the function tJ( T) and especially that of leA) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value t;; of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl' . . . , X , usually depends in n a complicated manner on the cyclic frequency). . This fact often presents difficulties in applying the obtained estimate t;; of the function I to the solution of specific problems rela ted to the process X . Theref ore, in practice, the t obtained values of the estimator t;; (or an estimator of the covariance function tJ~( T» are almost always "smoothed," i. e. , are approximated by values of a certain sufficiently simple function 1 = 1
Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series
DOWNLOAD
Author : K. Dzhaparidze
language : en
Publisher: Springer Science & Business Media
Release Date : 1986
Parameter Estimation And Hypothesis Testing In Spectral Analysis Of Stationary Time Series written by K. Dzhaparidze 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 1986 with Mathematics categories.
. . ) (under the assumption that the spectral density exists). For this reason, a vast amount of periodical and monographic literature is devoted to the nonparametric statistical problem of estimating the function tJ( T) and especially that of leA) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value t;; of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl' . . . , X , usually depends in n a complicated manner on the cyclic frequency). . This fact often presents difficulties in applying the obtained estimate t;; of the function I to the solution of specific problems rela ted to the process X . Theref ore, in practice, the t obtained values of the estimator t;; (or an estimator of the covariance function tJ~( T» are almost always "smoothed," i. e. , are approximated by values of a certain sufficiently simple function 1 = 1
A Handbook Of Time Series Analysis Signal Processing And Dynamics
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Author : D. S. G. Pollock
language : en
Publisher: Academic Press
Release Date : 1999
A Handbook Of Time Series Analysis Signal Processing And Dynamics written by D. S. G. Pollock and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
CD-ROM contains: Pascal and C code and programs -- bibliography of the book -- text of book -- tutorials.
Interpolation Of Spatial Data
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Author : Michael L. Stein
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Interpolation Of Spatial Data written by Michael L. Stein 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 2012-12-06 with Mathematics categories.
Prediction of a random field based on observations of the random field at some set of locations arises in mining, hydrology, atmospheric sciences, and geography. Kriging, a prediction scheme defined as any prediction scheme that minimizes mean squared prediction error among some class of predictors under a particular model for the field, is commonly used in all these areas of prediction. This book summarizes past work and describes new approaches to thinking about kriging.
Bayesian Forecasting And Dynamic Models
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Author : Mike West
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Bayesian Forecasting And Dynamic Models written by Mike West 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 2013-06-29 with Mathematics categories.
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Exponential Families Of Stochastic Processes
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Author : Uwe Küchler
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-09
Exponential Families Of Stochastic Processes written by Uwe Küchler 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 2006-05-09 with Mathematics categories.
Exponential families of stochastic processes are parametric stochastic p- cess models for which the likelihood function exists at all ?nite times and has an exponential representation where the dimension of the canonical statistic is ?nite and independent of time. This de?nition not only covers manypracticallyimportantstochasticprocessmodels,italsogivesrisetoa rather rich theory. This book aims at showing both aspects of exponential families of stochastic processes. Exponential families of stochastic processes are tractable from an a- lytical as well as a probabilistic point of view. Therefore, and because the theory covers many important models, they form a good starting point for an investigation of the statistics of stochastic processes and cast interesting light on basic inference problems for stochastic processes. Exponential models play a central role in classical statistical theory for independent observations, where it has often turned out to be informative and advantageous to view statistical problems from the general perspective of exponential families rather than studying individually speci?c expon- tial families of probability distributions. The same is true of stochastic process models. Thus several published results on the statistics of parti- lar process models can be presented in a uni?ed way within the framework of exponential families of stochastic processes.
Smoothing Methods In Statistics
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Author : Jeffrey S. Simonoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Smoothing Methods In Statistics written by Jeffrey S. Simonoff 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 2012-12-06 with Mathematics categories.
The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue.
Selected Papers Of Hirotugu Akaike
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Author : Emanuel Parzen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Selected Papers Of Hirotugu Akaike written by Emanuel Parzen 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 2012-12-06 with Mathematics categories.
The pioneering research of Hirotugu Akaike has an international reputation for profoundly affecting how data and time series are analyzed and modelled and is highly regarded by the statistical and technological communities of Japan and the world. His 1974 paper "A new look at the statistical model identification" (IEEE Trans Automatic Control, AC-19, 716-723) is one of the most frequently cited papers in the area of engineering, technology, and applied sciences (according to a 1981 Citation Classic of the Institute of Scientific Information). It introduced the broad scientific community to model identification using the methods of Akaike's criterion AIC. The AIC method is cited and applied in almost every area of physical and social science. The best way to learn about the seminal ideas of pioneering researchers is to read their original papers. This book reprints 29 papers of Akaike's more than 140 papers. This book of papers by Akaike is a tribute to his outstanding career and a service to provide students and researchers with access to Akaike's innovative and influential ideas and applications. To provide a commentary on the career of Akaike, the motivations of his ideas, and his many remarkable honors and prizes, this book reprints "A Conversation with Hirotugu Akaike" by David F. Findley and Emanuel Parzen, published in 1995 in the journal Statistical Science. This survey of Akaike's career provides each of us with a role model for how to have an impact on society by stimulating applied researchers to implement new statistical methods.
Statistical Methods In Software Engineering
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Author : Nozer D. Singpurwalla
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Statistical Methods In Software Engineering written by Nozer D. Singpurwalla 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 2012-12-06 with Computers categories.
This preface pertains to three issues that we would like to bring to the attention of the readers: our objectives, our intended audience, and the nature of the material. We have in mind several objectives. The first is to establish a framework for dealing with uncertainties in software engineering, and for using quantitative measures for decision making in this context. The second is to bring into perspective the large body of work having statistical content that is relevant to software engineering, which may not have appeared in the traditional outlets devoted to it. Connected with this second objective is a desire to streamline and organize our own thinking and work in this area. Our third objective is to provide a platform that facilitates an interface between computer scientists and statisticians to address a class of problems in computer science. It appears that such an interface is necessary to provide the needed synergism for solving some difficult problems that the subject poses. Our final objective is to serve as an agent for stimulating more cross-disciplinary research in computer science and statistics. To what extent the material here will meet our objectives can only be assessed with the passage of time. Our intended audience is computer scientists, software engineers, and reliability analysts, who have some exposure to probability and statistics. Applied statisticians interested in reliability problems are also a segment of our intended audience.