Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series write by K. Dzhaparidze. This book was released on 2012-12-06. Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series available in PDF, EPUB and Kindle. . . ) (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
Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series write by K. Dzhaparidze. This book was released on 1986. Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series available in PDF, EPUB and Kindle.
Parameter estimation and hypothesis testing in spectral analysis of stationary series
Parameter estimation and hypothesis testing in spectral analysis of stationary series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parameter estimation and hypothesis testing in spectral analysis of stationary series write by K. O. Džaparidze. This book was released on 1985. Parameter estimation and hypothesis testing in spectral analysis of stationary series available in PDF, EPUB and Kindle.
The Spectral Analysis of Time Series
The Spectral Analysis of Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Spectral Analysis of Time Series write by L. H. Koopmans. This book was released on 2014-05-12. The Spectral Analysis of Time Series available in PDF, EPUB and Kindle. The Spectral Analysis of Time Series describes the techniques and theory of the frequency domain analysis of time series. The book discusses the physical processes and the basic features of models of time series. The central feature of all models is the existence of a spectrum by which the time series is decomposed into a linear combination of sines and cosines. The investigator can used Fourier decompositions or other kinds of spectrals in time series analysis. The text explains the Wiener theory of spectral analysis, the spectral representation for weakly stationary stochastic processes, and the real spectral representation. The book also discusses sampling, aliasing, discrete-time models, linear filters that have general properties with applications to continuous-time processes, and the applications of multivariate spectral models. The text describes finite parameter models, the distribution theory of spectral estimates with applications to statistical inference, as well as sampling properties of spectral estimates, experimental design, and spectral computations. The book is intended either as a textbook or for individual reading for one-semester or two-quarter course for students of time series analysis users. It is also suitable for mathematicians or professors of calculus, statistics, and advanced mathematics.
Empirical Likelihood and Quantile Methods for Time Series
Empirical Likelihood and Quantile Methods for Time Series - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Empirical Likelihood and Quantile Methods for Time Series write by Yan Liu. This book was released on 2018-12-05. Empirical Likelihood and Quantile Methods for Time Series available in PDF, EPUB and Kindle. This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error. This is the first book to consider the generalized empirical likelihood applied to time series models in frequency domain and also the estimation motivated by minimizing quantile prediction error without assumption of true model. It provides the reader with a new horizon for understanding the prediction problem that occurs in time series modeling and a contemporary approach of hypothesis testing by the generalized empirical likelihood method. Nonparametric aspects of the methods proposed in this book also satisfactorily address economic and financial problems without imposing redundantly strong restrictions on the model, which has been true until now. Dealing with infinite variance processes makes analysis of economic and financial data more accurate under the existing results from the demonstrative research. The scope of applications, however, is expected to apply to much broader academic fields. The methods are also sufficiently flexible in that they represent an advanced and unified development of prediction form including multiple-point extrapolation, interpolation, and other incomplete past forecastings. Consequently, they lead readers to a good combination of efficient and robust estimate and test, and discriminate pivotal quantities contained in realistic time series models.
