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Constrained statistical inference : inequality, order, and shape restrictions / Mervyn J. Silvapulle, Pranab K. Sen
| Constrained statistical inference : inequality, order, and shape restrictions / Mervyn J. Silvapulle, Pranab K. Sen |
| Autore | Silvapulle, Mervyn J., 1951- |
| Pubbl/distr/stampa | Hoboken, N.J. : Wiley-Interscience, c2005 |
| Descrizione fisica | xvii, 532 p. : ill. ; 24 cm |
| Disciplina | 519.5/5 |
| Altri autori (Persone) | Sen, Pranab Kumar, 1937- |
| Collana | Wiley series in probability and statistics |
| Soggetto topico | Multivariate analysis |
| ISBN | 0471208272 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991002584999707536 |
Silvapulle, Mervyn J., 1951-
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| Hoboken, N.J. : Wiley-Interscience, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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A course in time series analysis [[electronic resource] /] / edited by Daniel Peña, George C. Tiao, Ruey S. Tsay
| A course in time series analysis [[electronic resource] /] / edited by Daniel Peña, George C. Tiao, Ruey S. Tsay |
| Pubbl/distr/stampa | New York, : J. Wiley, c2001 |
| Descrizione fisica | 1 online resource (494 p.) |
| Disciplina |
519.5/5
519.55 |
| Altri autori (Persone) |
PeñaDaniel <1948->
TiaoGeorge C. <1933-> TsayRuey S. <1951-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico | Time-series analysis |
| ISBN |
1-282-24252-0
9786613813640 1-118-03297-7 1-118-03122-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Basic concepts in univariate time series -- pt. 2. Advanced topics in univariate time series -- pt. 3. Multivariate time series. |
| Record Nr. | UNISA-996213510403316 |
| New York, : J. Wiley, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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A course in time series analysis / / edited by Daniel Pena, George C. Tiao, Ruey S. Tsay
| A course in time series analysis / / edited by Daniel Pena, George C. Tiao, Ruey S. Tsay |
| Pubbl/distr/stampa | New York, : J. Wiley, c2001 |
| Descrizione fisica | 1 online resource (494 p.) |
| Disciplina | 519.5/5 |
| Altri autori (Persone) |
PenaDaniel <1948->
TiaoGeorge C. <1933-> TsayRuey S. <1951-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico | Time-series analysis |
| ISBN |
1-282-24252-0
9786613813640 1-118-03297-7 1-118-03122-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Basic concepts in univariate time series -- pt. 2. Advanced topics in univariate time series -- pt. 3. Multivariate time series. |
| Record Nr. | UNINA-9910139189303321 |
| New York, : J. Wiley, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Efficient algorithms of time series processing and their applications [[electronic resource] /] / G. Sh. Tsitsiashvili, editor
| Efficient algorithms of time series processing and their applications [[electronic resource] /] / G. Sh. Tsitsiashvili, editor |
| Pubbl/distr/stampa | New York, : Nova Science Publishers, c2009 |
| Descrizione fisica | 1 online resource (109 p.) |
| Disciplina | 519.5/5 |
| Altri autori (Persone) | T͡Sit͡siashviliG. Sh (Gurami Shalvovich) |
| Soggetto topico |
Time-series analysis
Algorithms |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-61728-387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910460017403321 |
| New York, : Nova Science Publishers, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Efficient algorithms of time series processing and their applications [[electronic resource] /] / G. Sh. Tsitsiashvili, editor
| Efficient algorithms of time series processing and their applications [[electronic resource] /] / G. Sh. Tsitsiashvili, editor |
| Pubbl/distr/stampa | New York, : Nova Science Publishers, c2009 |
| Descrizione fisica | 1 online resource (109 p.) |
| Disciplina | 519.5/5 |
| Altri autori (Persone) | T͡Sit͡siashviliG. Sh (Gurami Shalvovich) |
| Soggetto topico |
Time-series analysis
Algorithms |
| ISBN | 1-61728-387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910785182503321 |
| New York, : Nova Science Publishers, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Efficient algorithms of time series processing and their applications / / G. Sh. Tsitsiashvili, editor
| Efficient algorithms of time series processing and their applications / / G. Sh. Tsitsiashvili, editor |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | New York, : Nova Science Publishers, c2009 |
| Descrizione fisica | 1 online resource (109 p.) |
| Disciplina | 519.5/5 |
| Altri autori (Persone) | T͡Sit͡siashviliG. Sh (Gurami Shalvovich) |
| Soggetto topico |
Time-series analysis
Algorithms |
| ISBN | 1-61728-387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- EFFICIENT ALGORITHMS OF TIME SERIES PROCESSING AND THEIR APPLICATIONS -- EFFICIENT ALGORITHMS OF TIME SERIES PROCESSING AND THEIR APPLICATIONS -- CONTENTS -- PREFACE -- Chapter 1.INTRA-ANNUAL BUNDLES OF CLIMATIC PARAMETERS -- ABSTRACT -- INTRODUCTION -- 1. DATA -- 2. METHODS INVESTIGATION -- 3. DISCUSSION OF RESULTS -- CONCLUSION -- REFERENCES -- Chapter 2. APPLICATION OF EXPERIENCE METHOD OF THE RECOGNITION BY INTERVAL FOR MAKING PROGNOSIS ON THE TATAR STRAIT(JAPAN SEA) ICE-COVER EXTREMITY -- ABSTRACT -- INTRODUCTION -- 1. DATA AND METHODS OF EXPLORATION -- 2. PECULIARITIES OF THE ATMOSPHERIC PROCESSES THAT PROVIDE FORMATION OF ANOMALOUS ICE COVER IN THE TATAR STRAIT -- 3. RESULTS OF CALCULATIONS -- CONCLUSION -- REFERENCES -- Chapter 3. FACTOR TEMPORAL PROGNOSIS OF CRITICAL LEVELS OF HUMAN INFECTION RATE -- REFERENCES -- Chapter 4SPACE-TIME PROGNOSIS OF TICK-BORNE ENCEPHALITIS FOCI FUNCTIONING -- REFERENCES -- Chapter 5. SYSTEM APPROACH IN DEMOGRAPHIC INVESTIGATIONS -- ABSTRACT -- MORTALITY IN PRIMORSKIY KRAY DEPENDING ON AGE -- REFERENCES -- Chapter 6. THE DETERMINATION OF FIXITY FACTORS IN DYNAMIC RISE OF CITIES -- ABSTRACT -- REFERENCES -- Chapter 7. EXPLORATION OF VARIABILITY IN THE ABOVE-EARTH AIR TEMPERATURE OVER THE FAR EAST REGIONS BY THE METHOD OF RESIDUAL VARIABILITY OF TEMPORAL ROW -- ABSTRACT -- 1. DATA AND METHOD OF EXPLORATION -- 2. PRINCIPAL RESULTS OF RESEARCH -- CONCLUSION -- REFERENCES -- Chapter 8. ESTIMATES OF VARIANCES IN TIME SERIES STATISTICS -- 1. MODIFIED EMPIRICAL VARIANCE -- 2. VARIATION OF DEVIATION FROM POLYNOMIAL REGRESSION FUNCTION -- 3. MODIFIED EMPIRICAL COVARIANCE -- REFERENCES -- INDEX -- Blank Page. |
| Record Nr. | UNINA-9910816861603321 |
| New York, : Nova Science Publishers, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Forecasting with dynamic regression models [[electronic resource] /] / Alan Pankratz
| Forecasting with dynamic regression models [[electronic resource] /] / Alan Pankratz |
| Autore | Pankratz Alan <1944-> |
| Pubbl/distr/stampa | New York, : John Wiley & Sons, 1991 |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Wiley series in probability and mathematical statistics. Applied probability and statistics |
| Soggetto topico |
Time-series analysis
Regression analysis Prediction theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-44612-X
9786613446121 1-118-15052-X 1-118-15078-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Forecasting with Dynamic Regression Models; Contents; Preface; Chapter 1 Introduction and Overview; 1.1 Related Time Series; 1.2 Overview: Dynamic Regression Models; 1.3 Box and Jenkins' Modeling Strategy; 1.4 Correlation; 1.5 Layout of the Book; Questions and Problems; Chapter 2 A Primer on ARIMA Models; 2.1 Introduction; 2.2 Stationary Variance and Mean; 2.3 Autocorrelation; 2.4 Five Stationary ARIMA Processes; 2.5 ARIMA Modeling in Practice; 2.6 Backshift Notation; 2.7 Seasonal Models; 2.8 Combined Nonseasonal and Seasonal Processes; 2.9 Forecasting; 2.10 Extended Autocorrelation Function
2.11 Interpreting ARIMA Model ForecastsQuestions and Problems; Case 1 Federal Government Receipts (ARIMA); Chapter 3 A Primer on Regression Models; 3.1 Two Types of Data; 3.2 The Population Regression Function (PRF) with One Input; 3.3 The Sample Regression Function (SRF) with One Input; 3.4 Properties of the Least-Squares Estimators; 3.5 Goodness of Fit (R2); 3.6 Statistical Inference; 3.7 Multiple Regression; 3.8 Selected Issues in Regression; 3.9 Application to Time Series Data; Questions and Problems; Case 2 Federal Government Receipts (Dynamic Regression); Case 3 Kilowatt-Hours Used Chapter 4 Rational Distributed Lag Models4.1 Linear Distributed Lag Transfer Functions; 4.2 A Special Case: The Koyck Model; 4.3 Rational Distributed Lags; 4.4 The Complete Rational Form DR Model and Some Special Cases 163; Questions and Problems; Chapter 5 Building Dynamic Regression Models: Model Identification; 5.1 Overview; 5.2 Preliminary Modeling Steps; 5.3 The Linear Transfer Function (LTF) Identification Method; 5.4 Rules for Identifying Rational Distributed Lag Transfer Functions; Questions and Problems; Appendix 5A The Corner Table Appendix 5B Transfer Function Identification Using Prewhitening and Cross CorrelationsChapter 6 Building Dynamic Regression Models: Model Checking, Reformulation and Evaluation; 6.1 Diagnostic Checking and Model Reformulation; 6.2 Evaluating Estimation Stage Results; Questions and Problems; Case 4 Housing Starts and Sales; Case 5 Industrial Production, Stock Prices, and Vendor Performance; Chapter 7 Intervention Analysis; 7.1 Introduction; 7.2 Pulse Interventions; 7.3 Step Interventions; 7.4 Building Intervention Models; 7.5 Multiple and Compound Interventions; Questions and Problems Case 6 Year-End LoadingChapter 8 Intervention and Outlier Detection and Treatment; 8.1 The Rationale for Intervention and Outlier Detection; 8.2 Models for Intervention and Outlier Detection; 8.3 Likelihood Ratio Criteria; 8.4 An Iterative Detection Procedure; 8.5 Application; 8.6 Detected Events Near the End of a Series; Questions and Problems; Appendix 8A BASIC Program to Detect AO, LS, and IO Events; Appendix 8B Specifying IO Events in the SCA System; Chapter 9 Estimation and Forecasting; 9.1 DR Model Estimation; 9.2 Forecasting; Questions and Problems Appendix 9A A BASIC Routine for Computing the Nonbiasing Factor in (9.2.24) |
| Record Nr. | UNINA-9910139721303321 |
|
Pankratz Alan <1944->
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||
| New York, : John Wiley & Sons, 1991 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting with dynamic regression models [[electronic resource] /] / Alan Pankratz
| Forecasting with dynamic regression models [[electronic resource] /] / Alan Pankratz |
| Autore | Pankratz Alan <1944-> |
| Pubbl/distr/stampa | New York, : John Wiley & Sons, 1991 |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina |
519.5/5
519.55 |
| Collana | Wiley series in probability and mathematical statistics. Applied probability and statistics |
| Soggetto topico |
Time-series analysis
Regression analysis Prediction theory |
| ISBN |
1-283-44612-X
9786613446121 1-118-15052-X 1-118-15078-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Forecasting with Dynamic Regression Models; Contents; Preface; Chapter 1 Introduction and Overview; 1.1 Related Time Series; 1.2 Overview: Dynamic Regression Models; 1.3 Box and Jenkins' Modeling Strategy; 1.4 Correlation; 1.5 Layout of the Book; Questions and Problems; Chapter 2 A Primer on ARIMA Models; 2.1 Introduction; 2.2 Stationary Variance and Mean; 2.3 Autocorrelation; 2.4 Five Stationary ARIMA Processes; 2.5 ARIMA Modeling in Practice; 2.6 Backshift Notation; 2.7 Seasonal Models; 2.8 Combined Nonseasonal and Seasonal Processes; 2.9 Forecasting; 2.10 Extended Autocorrelation Function
2.11 Interpreting ARIMA Model ForecastsQuestions and Problems; Case 1 Federal Government Receipts (ARIMA); Chapter 3 A Primer on Regression Models; 3.1 Two Types of Data; 3.2 The Population Regression Function (PRF) with One Input; 3.3 The Sample Regression Function (SRF) with One Input; 3.4 Properties of the Least-Squares Estimators; 3.5 Goodness of Fit (R2); 3.6 Statistical Inference; 3.7 Multiple Regression; 3.8 Selected Issues in Regression; 3.9 Application to Time Series Data; Questions and Problems; Case 2 Federal Government Receipts (Dynamic Regression); Case 3 Kilowatt-Hours Used Chapter 4 Rational Distributed Lag Models4.1 Linear Distributed Lag Transfer Functions; 4.2 A Special Case: The Koyck Model; 4.3 Rational Distributed Lags; 4.4 The Complete Rational Form DR Model and Some Special Cases 163; Questions and Problems; Chapter 5 Building Dynamic Regression Models: Model Identification; 5.1 Overview; 5.2 Preliminary Modeling Steps; 5.3 The Linear Transfer Function (LTF) Identification Method; 5.4 Rules for Identifying Rational Distributed Lag Transfer Functions; Questions and Problems; Appendix 5A The Corner Table Appendix 5B Transfer Function Identification Using Prewhitening and Cross CorrelationsChapter 6 Building Dynamic Regression Models: Model Checking, Reformulation and Evaluation; 6.1 Diagnostic Checking and Model Reformulation; 6.2 Evaluating Estimation Stage Results; Questions and Problems; Case 4 Housing Starts and Sales; Case 5 Industrial Production, Stock Prices, and Vendor Performance; Chapter 7 Intervention Analysis; 7.1 Introduction; 7.2 Pulse Interventions; 7.3 Step Interventions; 7.4 Building Intervention Models; 7.5 Multiple and Compound Interventions; Questions and Problems Case 6 Year-End LoadingChapter 8 Intervention and Outlier Detection and Treatment; 8.1 The Rationale for Intervention and Outlier Detection; 8.2 Models for Intervention and Outlier Detection; 8.3 Likelihood Ratio Criteria; 8.4 An Iterative Detection Procedure; 8.5 Application; 8.6 Detected Events Near the End of a Series; Questions and Problems; Appendix 8A BASIC Program to Detect AO, LS, and IO Events; Appendix 8B Specifying IO Events in the SCA System; Chapter 9 Estimation and Forecasting; 9.1 DR Model Estimation; 9.2 Forecasting; Questions and Problems Appendix 9A A BASIC Routine for Computing the Nonbiasing Factor in (9.2.24) |
| Record Nr. | UNINA-9910829964503321 |
|
Pankratz Alan <1944->
|
||
| New York, : John Wiley & Sons, 1991 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting with dynamic regression models / / Alan Pankratz
| Forecasting with dynamic regression models / / Alan Pankratz |
| Autore | Pankratz Alan <1944-> |
| Pubbl/distr/stampa | New York, : John Wiley & Sons, 1991 |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina | 519.5/5 |
| Collana | Wiley series in probability and mathematical statistics. Applied probability and statistics |
| Soggetto topico |
Time-series analysis
Regression analysis Prediction theory |
| ISBN |
1-283-44612-X
9786613446121 1-118-15052-X 1-118-15078-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Forecasting with Dynamic Regression Models; Contents; Preface; Chapter 1 Introduction and Overview; 1.1 Related Time Series; 1.2 Overview: Dynamic Regression Models; 1.3 Box and Jenkins' Modeling Strategy; 1.4 Correlation; 1.5 Layout of the Book; Questions and Problems; Chapter 2 A Primer on ARIMA Models; 2.1 Introduction; 2.2 Stationary Variance and Mean; 2.3 Autocorrelation; 2.4 Five Stationary ARIMA Processes; 2.5 ARIMA Modeling in Practice; 2.6 Backshift Notation; 2.7 Seasonal Models; 2.8 Combined Nonseasonal and Seasonal Processes; 2.9 Forecasting; 2.10 Extended Autocorrelation Function
2.11 Interpreting ARIMA Model ForecastsQuestions and Problems; Case 1 Federal Government Receipts (ARIMA); Chapter 3 A Primer on Regression Models; 3.1 Two Types of Data; 3.2 The Population Regression Function (PRF) with One Input; 3.3 The Sample Regression Function (SRF) with One Input; 3.4 Properties of the Least-Squares Estimators; 3.5 Goodness of Fit (R2); 3.6 Statistical Inference; 3.7 Multiple Regression; 3.8 Selected Issues in Regression; 3.9 Application to Time Series Data; Questions and Problems; Case 2 Federal Government Receipts (Dynamic Regression); Case 3 Kilowatt-Hours Used Chapter 4 Rational Distributed Lag Models4.1 Linear Distributed Lag Transfer Functions; 4.2 A Special Case: The Koyck Model; 4.3 Rational Distributed Lags; 4.4 The Complete Rational Form DR Model and Some Special Cases 163; Questions and Problems; Chapter 5 Building Dynamic Regression Models: Model Identification; 5.1 Overview; 5.2 Preliminary Modeling Steps; 5.3 The Linear Transfer Function (LTF) Identification Method; 5.4 Rules for Identifying Rational Distributed Lag Transfer Functions; Questions and Problems; Appendix 5A The Corner Table Appendix 5B Transfer Function Identification Using Prewhitening and Cross CorrelationsChapter 6 Building Dynamic Regression Models: Model Checking, Reformulation and Evaluation; 6.1 Diagnostic Checking and Model Reformulation; 6.2 Evaluating Estimation Stage Results; Questions and Problems; Case 4 Housing Starts and Sales; Case 5 Industrial Production, Stock Prices, and Vendor Performance; Chapter 7 Intervention Analysis; 7.1 Introduction; 7.2 Pulse Interventions; 7.3 Step Interventions; 7.4 Building Intervention Models; 7.5 Multiple and Compound Interventions; Questions and Problems Case 6 Year-End LoadingChapter 8 Intervention and Outlier Detection and Treatment; 8.1 The Rationale for Intervention and Outlier Detection; 8.2 Models for Intervention and Outlier Detection; 8.3 Likelihood Ratio Criteria; 8.4 An Iterative Detection Procedure; 8.5 Application; 8.6 Detected Events Near the End of a Series; Questions and Problems; Appendix 8A BASIC Program to Detect AO, LS, and IO Events; Appendix 8B Specifying IO Events in the SCA System; Chapter 9 Estimation and Forecasting; 9.1 DR Model Estimation; 9.2 Forecasting; Questions and Problems Appendix 9A A BASIC Routine for Computing the Nonbiasing Factor in (9.2.24) |
| Record Nr. | UNINA-9910876825903321 |
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Pankratz Alan <1944->
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| New York, : John Wiley & Sons, 1991 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fourier analysis of time series [[electronic resource] ] : an introduction / / Peter Bloomfield
| Fourier analysis of time series [[electronic resource] ] : an introduction / / Peter Bloomfield |
| Autore | Bloomfield Peter <1946-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | New York, : Wiley, c2000 |
| Descrizione fisica | 1 online resource (285 p.) |
| Disciplina |
515.2433
519.5/5 519.55 |
| Collana | Wiley series in probability and statistics. Applied probability and statistics section |
| Soggetto topico |
Time-series analysis
Fourier analysis |
| ISBN |
1-280-54195-4
9786610541959 0-471-65399-3 0-471-72223-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; 1 Introduction; 1.1 Fourier Analysis; 1.2 Historical Development of Fourier Methods; 1.3 Why Use Trigonometric Functions?; 2 Fitting Sinusoids; 2.1 Curve-Fitting Approach; 2.2 Least Squares Fitting of Sinusoids; 2.3 Multiple Periodicities; 2.4 Orthogonality of Sinusoids; 2.5 Effect of Discrete Time: Aliasing; 2.6 Some Statistical Results; Appendix; 3 The Search for Periodicity; 3.1 Fitting the Frequency; 3.2 Fitting Multiple Frequencies; 3.3 Some More Statistical Results; Appendix; 4 Harmonic Analysis; 4.1 Fourier Frequencies; 4.2 Discrete Fourier Transform
4.3 Decomposing the Sum of Squares4.4 Special Functions; 4.5 Smooth Functions; 5 The Fast Fourier Transform; 5.1 Computational Cost of Fourier Transforms; 5.2 Two-Factor Case; 5.3 Application to Harmonic Analysis of Data; 6 Examples of Harmonic Analysis; 6.1 Variable Star Data; 6.2 Leakage Reduction by Data Windows; 6.3 Tapering the Variable Star Data; 6.4 Wolf's Sunspot Numbers; 6.5 Nonsinusoidal Oscillations; 6.6 Amplitude and Phase Fluctuations; 6.7 Transformations; 6.8 Periodogram of a Noise Series; 6.9 Fisher's Test for Periodicity; Appendix; 7 Complex Demodulation; 7.1 Introduction 7.2 Smoothing: Linear Filtering7.3 Designing a Filter; 7.4 Least Squares Filter Design; 7.5 Demodulating the Sunspot Series; 7.6 Complex Time Series; 7.7 Sunspots: The Complex Series; Appendix; 8 The Spectrum; 8.1 Periodogram Analysis of Wheat Prices; 8.2 Analysis of Segments of a Series; 8.3 Smoothing the Periodogram; 8.4 Autocovariances and Spectrum Estimates; 8.5 Alternative Representations; 8.6 Choice of a Spectral Window; 8.7 Examples of Smoothing the Periodogram; 8.8 Reroughing the Spectrum; Appendix; 9 Some Stationary Time Series Theory; 9.1 Stationary Time Series 9.2 Continuous Spectra9.3 Time Averaging and Ensemble Averaging; 9.4 Periodogram and Continuous Spectra; 9.5 Approximate Mean and Variance; 9.6 Properties of Spectral Windows; 9.7 Aliasing and the Spectrum; 10 Analysis of Multiple Series; 10.1 Cross Periodogram; 10.2 Estimating the Cross Spectrum; 10.3 Theoretical Cross Spectrum; 10.4 Distribution of the Cross Periodogram; 10.5 Distribution of Estimated Cross Spectra; 10.6 Alignment; Appendix; 11 Further Topics; 11.1 Time Domain Analysis; 11.2 Spatial Series; 11.3 Multiple Series; 11.4 Higher Order Spectra 11.5 Nonquadratic Spectrum Estimates11.6 Incomplete and Irregular Data; References; Author Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; Subject Index; A; B; C; D; E; F; G; H; I; J; L; M; N; O; P; Q; R; S; T; V; W |
| Record Nr. | UNINA-9910143508503321 |
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Bloomfield Peter <1946->
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| New York, : Wiley, c2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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