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049 |a UAMI 
100 1 |a Kashyap, Rangasami L.  |q (Rangasami Laksminarayana),  |d 1938- 
245 1 0 |a Dynamic stochastic models from empirical data /  |c R.L. Kashyap, A. Ramachandra Rao. 
260 |a New York :  |b Academic Press,  |c 1976. 
300 |a 1 online resource (xvi, 334 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Mathematics in science and engineering ;  |v v. 122 
504 |a Includes bibliographical references (pages 325-330) and index. 
588 0 |a Print version record. 
506 |3 Use copy  |f Restrictions unspecified  |2 star  |5 MiAaHDL 
533 |a Electronic reproduction.  |b [Place of publication not identified] :  |c HathiTrust Digital Library,  |d 2010.  |5 MiAaHDL 
538 |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.  |u http://purl.oclc.org/DLF/benchrepro0212  |5 MiAaHDL 
583 1 |a digitized  |c 2010  |h HathiTrust Digital Library  |l committed to preserve  |2 pda  |5 MiAaHDL 
505 0 |a Front Cover; Dynamic Stochastic Models from Empirical Data; Copyright Page; Contents; Preface; Acknowledgments; Notation and Symbols; CHAPTER I. INTRODUCTION TO THE CONSTRUCTION OF MODELS; 1a. Nature and Goals of Modeling; 1b. Description of Models; 1c. Choice of a Model for the Given Data; 1d. Validation; Notes; CHAPTER II. PRELIMINARY ANALYSIS OF STOCHASTIC DYNAMICAL SYSTEMS; Introduction; 2a. Assumptions and Discussion; 2b. Stationarity; 2c. Invertibility; 2d. Covariance Functions and Correlograms; 2e. Spectral Analysis; 2f. Prediction; 2g. Prediction in Multiplicative Systems 
505 8 |a 2h. Prediction in Systems with Noisy Observations2i. Rescaled Range-Lag Characteristic; 2j. Fractional Noise Models; 2k. Conclusions; Appendix 2.1. Characteristics of Fractional Noise Models; Problems; CHAPTER III. STRUCTURE OF UNIVARIATE MODELS; Introduction; 3a. Types of Dynamic Stochastic Models; 3b. Types of Empirical Time Series; 3c. Causality; 3d. Choice of Time Scale for Modeling; 3e. Conclusions; Notes; Problems; CHAPTER IV. ESTIMABILITY IN SINGLE OUTPUT SYSTEMS; Introduction; 4a. Estimability of Systems in Standard Form; 4b. Estimability in Systems with Noisy Observations 
505 8 |a 4c. Estimability in Systems with AR Disturbances4d. The Estimation Accuracy; 4e. Conclusions; Appendix 4.1; Appendix 4.2. Evaluation of the Cramér-Rao Matrix Lower Bound in Single Output Systems; Problems; CHAPTER V. STRUCTURE AND ESTIMABILITY IN MULTIVARIATE SYSTEMS; Introduction; 5a. Characterization; 5b. The Triangular Canonical Forms; 5c. Diagonal Canonical Forms; 5d. Pseudocanonical Forms; 5e. Discussion of the Three Canonical Forms; 5f. Estimation Accuracy; 5g. Conclusions; Appendix 5.1. Proofs of Theorems; Problems; CHAPTER VI. ESTIMATION IN AUTOREGRESSIVE PROCESSES; Introduction 
505 8 |a 6a. Maximum Likelihood Estimators6b. Bayesian Estimators; 6c. Quasi-Maximum Likelihood (QML) Estimators in Single Output Systems; 6d. Computational Methods; 6e. Combined Parameter Estimation and Prediction; 6f. Systems with Slowly Varying Coefficients; 6g. Robust Estimation in AR Models; 6h. Conclusions; Appendix 6.1. Proofs of Theorems in Section 6a; Appendix 6.2. The Expressions for the Posterior Densities; Appendix 6.3. The Derivation of Computational Algorithms; Appendix 6.4. Evaluation of the Cramér-Rao Lower Bound in Multi- variate AR Systems; Problems 
505 8 |a CHAPTER VII. PARAMETER ESTIMATION IN SYSTEMS WITH BOTH MOVING AVERAGE AND AUTOREGRESSIVE TERMSIntroduction; 7a. Maximum Likelihood Estimators; 7b. Numerical Methods for CML Estimation; 7c. Limited Information Estimates; 7d. Numerical Experiments with Estimation Methods; 7e. Conclusions; Problems; CHAPTER VIII. CLASS SELECTION AND VALIDATION OF UNIVARIATE MODELS; Introduction; 8a. The Nature of the Selection Problem; 8b. The Different Methods of Class Selection; 8c. Validation of Fitted Models; 8d. Discussion of Selection and Validation; 8e. Conclusions 
520 |a Dynamic stochastic models from empirical data. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Time-series analysis. 
650 0 |a Stochastic processes. 
650 0 |a Estimation theory. 
650 0 |a System analysis. 
650 2 |a Stochastic Processes 
650 2 |a Systems Analysis 
650 6 |a Série chronologique. 
650 6 |a Processus stochastiques. 
650 6 |a Théorie de l'estimation. 
650 6 |a Analyse de systèmes. 
650 7 |a systems analysis.  |2 aat 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
650 7 |a Estimation theory  |2 fast 
650 7 |a Stochastic processes  |2 fast 
650 7 |a System analysis  |2 fast 
650 7 |a Time-series analysis  |2 fast 
650 7 |a Processus stochastiques.  |2 ram 
650 7 |a Systèmes, Analyse de.  |2 ram 
650 7 |a Séries chronologiques.  |2 ram 
700 1 |a Rao, A. Ramachandra  |q (Adiseshappa Ramachandra),  |d 1939- 
776 0 8 |i Print version:  |a Kashyap, Rangasami L. (Rangasami Laksminarayana), 1938-  |t Dynamic stochastic models from empirical data.  |d New York : Academic Press, 1976  |z 9780124005501  |w (DLC) 75013093  |w (OCoLC)1975888 
830 0 |a Mathematics in science and engineering ;  |v v. 122. 
856 4 0 |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297096  |z Texto completo 
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938 |a Internet Archive  |b INAR  |n dynamicstochasti0122kash 
994 |a 92  |b IZTAP