05416nmm a2200337 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100002400139245013100163250001700294260004800311300003300359505097300392505075901365505097202124653003503096653001703131653002303148653001803171700003403189041001903223989003803242490003403280028003003314856007203344082001003416520165203426EB000618483EBX0100000000000000047156500000000000000.0cr|||||||||||||||||||||140122 ||| eng a97814612116241 aTaniguchi, Masanobu00aAsymptotic Theory of Statistical Inference for Time SerieshElektronische Ressourcecby Masanobu Taniguchi, Yoshihide Kakizawa a1st ed. 2000 aNew York, NYbSpringer New Yorkc2000, 2000 aXVII, 662 pbonline resource0 a4.6 Higher Order Asymptotic Theory for Normalizing Transformations -- 4.7 Generalization of LeCam’s Third Lemma and Higher Order Asymptotics of Iterative Methods -- Problems -- 5 Asymptotic Theory for Long-Memory Processes -- 5.1 Some Elements of Long-Memory Processes -- 5.2 Limit Theorems for Fundamental Statistics -- 5.3 Estimation and Testing Theory for Long-Memory Processes -- 5.4 Regression Models with Long-Memory Disturbances -- 5.5 Semiparametric Analysis and the LAN Approach -- Problems -- 6 Statistical Analysis Based on Functionals of Spectra -- 6.1 Estimation of Nonlinear Functionals of Spectra -- 6.2 Application to Parameter Estimation for Stationary Processes -- 6.3 Asymptotically Efficient Nonparametric Estimation of Functionals of Spectra in Gaussian Stationary Processes -- 6.4 Robustness in the Frequency Domain Approach -- 6.5 NumericalExamples -- Problems -- 7 Discriminant Analysis for Stationary Time Series -- 7.1 Basic Formulation -- 0 a7.2 Standard Methods for Gaussian Stationary Processes -- 7.3 Discriminant Analysis for Non-Gaussian Linear Processes -- 7.4 Nonparametric Approach for Discriminant Analysis -- 7.5 Parametric Approach for Discriminant Analysis -- 7.6 Derivation of Spectral Expressions to Divergence Measures Between Gaussian Stationary Processes -- 7.7 Miscellany -- Problems -- 8 Large Deviation Theory and Saddlepoint Approximation for Stochastic Processes -- 8.1 Large Deviation Theorem 538 8.2 Asymptotic Efficiency for Gaussian Stationary Processes:Large Deviation Approach -- 8.3 Large Deviation Results for an Ornstein-Uhlenbeck Process -- 8.4 Saddlepoint Approximations for Stochastic Processes -- Problems -- A.1 Mathematics -- A.2 Probability -- A.3 Statistics0 a1 Elements of Stochastic Processes -- 1.1 Introduction -- 1.2 Stochastic Processes -- 1.3 Limit Theorems -- Problems -- 2 Local Asymptotic Normality for Stochastic Processes -- 2.1 General Results for Local Asymptotic Normality -- 2.2 Local Asymptotic Normality for Linear Processes -- Problems -- 3 Asymptotic Theory of Estimation and Testing for Stochastic Processes -- 3.1 Asymptotic Theory of Estimation and Testing for Linear Processes -- 3.2 Asymptotic Theory for Nonlinear Stochastic Models -- 3.3 Asymptotic Theory for Continuous Time Processes -- Problems -- 4 Higher Order Asymptotic Theory for Stochastic Processes -- 4.1 Introduction to Higher Order Asymptotic Theory -- 4.2 Valid Asymptotic Expansions -- 4.3 Higher Order Asymptotic Estimation Theory for Discrete Time Processes in View of Statistical Differential Geometry -- 4.4 Higher Order Asymptotic Theory for Continuous Time Processes -- 4.5 Higher Order Asymptotic Theory for Testing Problems -- aStatistical Theory and Methods aStatistics aProbability Theory aProbabilities1 aKakizawa, Yoshihidee[author]07aeng2ISO 639-2 bSBAaSpringer Book Archives -20040 aSpringer Series in Statistics50a10.1007/978-1-4612-1162-440uhttps://doi.org/10.1007/978-1-4612-1162-4?nosfx=yxVerlag3Volltext0 a519.5 aThere has been much demand for the statistical analysis of dependent ob servations in many fields, for example, economics, engineering and the nat ural sciences. A model that describes the probability structure of a se ries of dependent observations is called a stochastic process. The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. We deal with a wide variety of stochastic processes, for example, non-Gaussian linear processes, long-memory processes, nonlinear processes, orthogonal increment process es, and continuous time processes. For them we develop not only the usual estimation and testing theory but also many other statistical methods and techniques, such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of differential geometry, large deviation principle, and saddlepoint approximation. Because it is d ifficult to use the exact distribution theory, the discussion is based on the asymptotic theory. Optimality of various procedures is often shown by use of local asymptotic normality (LAN), which is due to LeCam. This book is suitable as a professional reference book on statistical anal ysis of stochastic processes or as a textbook for students who specialize in statistics. It will also be useful to researchers, including those in econo metrics, mathematics, and seismology, who utilize statistical methods for stochastic processes