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Adaptive Filtering Prediction and Control
| Adaptive Filtering Prediction and Control |
| Autore | Goodwin Graham C (Graham Clifford), <1945-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newburyport, : Dover Publications, 2014 |
| Descrizione fisica | 1 online resource (1123 p.) |
| Disciplina | 003/.83 |
| Altri autori (Persone) | SinKwai Sang |
| Collana | Dover Books on Electrical Engineering |
| Soggetto topico |
Discrete-time systems
Filters (Mathematics) Prediction theory Control theory Civil & Environmental Engineering Engineering & Applied Sciences Operations Research |
| ISBN |
0-486-13772-4
1-62870-072-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright Page; Table of Contents; Preface; 1 Introduction To Adaptive Techniques; 1.1 Filtering; 1.2 Prediction; 1.3 Control; Part I: Deterministic Systems; 2 Models for Deterministic Dynamical Systems; 2.1 Introduction; 2.2 State-Space Models; 2.2.1 General; 2.2.2 Controllable State-Space Models; 2.2.3 Observable State-Space Models; 2.2.4 Minimal State-Space Models; 2.3 Difference Operator Representations; 2.3.1 General; 2.3.2 Right Difference Operator Representations; 2.3.3 Left Difference Operator Representations; 2.3.4 Deterministic Autoregressive Moving-Average Models
2.3.5 Irreducible Difference Operator Representations2.4 Models for Bilinear Systems; 3 Parameter Estimation for Deterministic Systems; 3.1 Introduction; 3.2 On-Line Estimation Schemes; 3.3 Equation Error Methods for Deterministic Systems; 3.4 Parameter Convergence; 3.4.1 The Orthogonalized Projection Algorithm; 3.4.2 The Least-Squares Algorithm; 3.4.3 The Projection Algorithm; 3.4.4 Persistent Excitation; 3.5 Output Error Methods; 3.6 Parameter Estimation with Bounded Noise; 3.7 Constrained Parameter Estimation; 3.8 Parameter Estimation for Multi-output Systems; 3.9 Concluding Remarks 4 Deterministic Adaptive Prediction4.1 Introduction; 4.2 Predictor Structures; 4.2.1 Prediction with Known Models; 4.2.2 Restricted Complexity Predictors; 4.3 Adaptive Prediction; 4.3.1 Direct Adaptive Prediction; 4.3.2 Indirect Adaptive Prediction; 4.4 Concluding Remarks; 5 Control of Linear Deterministic Systems; 5.1 Introduction; 5.2 Minimum Prediction Error Controllers; 5.2.1 One-Step-Ahead Control (The SISO Case); 5.2.2 Model Reference Control (The SISO Case); 5.2.3 One-Step-Ahead Design for Multi-input Multi-output Systems; 5.2.4 Robustness Considerations 5.3 Closed-Loop Pole Assignment5.3.1 Introduction; 5.3.2 The Pole Assignment Algorithm (Difference Operator Formulation); 5.3.3 Rapprochement with State- Variable Feedback; 5.3.4 Rapprochement with Minimum Prediction Error Control; 5.3.5 The Internal Model Principle; 5.3.6 Some Design Considerations; 5.4 An Illustrative Example; 6 Adaptive Control Of Linear Deterministic Systems; 6.1 Introduction; 6.2 The Key Technical Lemma; 6.3 Minimum Prediction Error Adaptive Controllers (Direct Approach); 6.3.1 One-Step-Ahead Adaptive Control (The SISO Case); 6.3.2 Model Reference Adaptive Control 6.3.3 One-Step-Ahead Adaptive Controllers for Multi-input Multi-output Systems6.4 Minimum Prediction Error Adaptive Controllers (Indirect Approach); 6.5 Adaptive Algorithms for Closed-Loop Pole Assignment; 6.6 Adaptive Control of Nonlinear Systems; 6.7 Adaptive Control of Time-Varying Systems; 6.8 Some Implementation Considerations; Part II: Stochastic Systems; 7 Optimal Filtering and Prediction; 7.1 Introduction; 7.2 Stochastic State-Space Models; 7.3 Linear Optimal Filtering and Prediction; 7.3.1 The Kalman Filter; 7.3.2 Fixed-Lag Smoothing; 7.3.3 Fixed-Point Smoothing 7.3.4 Optimal Prediction |
| Record Nr. | UNINA-9911007277703321 |
Goodwin Graham C (Graham Clifford), <1945->
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| Newburyport, : Dover Publications, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Application of quantitative techniques for the prediction of bank acquisition targets [[electronic resource] /] / Fotios Pasiouras, Sailesh Tanna, Constantin Zopounidis
| Application of quantitative techniques for the prediction of bank acquisition targets [[electronic resource] /] / Fotios Pasiouras, Sailesh Tanna, Constantin Zopounidis |
| Autore | Pasiouras Fotios |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific, c2005 |
| Descrizione fisica | 1 online resource (293 p.) |
| Disciplina | 332.1 |
| Altri autori (Persone) |
TannaSailesh
ZopounidisConstantin |
| Collana | Series on computers and operations research |
| Soggetto topico |
Bank mergers - Econometric models
Prediction theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-37288-9
9786611372880 981-270-320-9 |
| Classificazione | 83.44 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface; Contents; 1. Banks M&As: Motives and Evidence; 2. Studies on the Prediction of Acquisition Targets; 3 . Methodological Framework for the Development of Acquisition Targets Prediction Model; 4 . Data and Preliminary Analysis; 5 . Development of Acquisitions Prediction Models; 6 . Integration of Prediction Models; 7 . Conclusions; Bibliography; Index |
| Record Nr. | UNINA-9910450730503321 |
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Pasiouras Fotios
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| Singapore ; ; Hackensack, NJ, : World Scientific, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Application of quantitative techniques for the prediction of bank acquisition targets [[electronic resource] /] / Fotios Pasiouras, Sailesh Tanna, Constantin Zopounidis
| Application of quantitative techniques for the prediction of bank acquisition targets [[electronic resource] /] / Fotios Pasiouras, Sailesh Tanna, Constantin Zopounidis |
| Autore | Pasiouras Fotios |
| Pubbl/distr/stampa | Singapore ; ; Hackensack, NJ, : World Scientific, c2005 |
| Descrizione fisica | 1 online resource (293 p.) |
| Disciplina | 332.1 |
| Altri autori (Persone) |
TannaSailesh
ZopounidisConstantin |
| Collana | Series on computers and operations research |
| Soggetto topico |
Bank mergers - Econometric models
Prediction theory |
| ISBN |
1-281-37288-9
9786611372880 981-270-320-9 |
| Classificazione | 83.44 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface; Contents; 1. Banks M&As: Motives and Evidence; 2. Studies on the Prediction of Acquisition Targets; 3 . Methodological Framework for the Development of Acquisition Targets Prediction Model; 4 . Data and Preliminary Analysis; 5 . Development of Acquisitions Prediction Models; 6 . Integration of Prediction Models; 7 . Conclusions; Bibliography; Index |
| Record Nr. | UNINA-9910784047403321 |
|
Pasiouras Fotios
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| Singapore ; ; Hackensack, NJ, : World Scientific, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose
| Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2015 |
| Descrizione fisica | 1 online resource (827 p.) |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico |
Data mining
Prediction theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-86870-6
1-118-86867-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Contents; Preface; Acknowledgments; Part I Data Preparation; Chapter 1 An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need for Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.4.1 CRISP-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; The R Zone; R References; Exercises
Chapter 2 Data Preprocessing2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data; 2.4 Identifying Misclassifications; 2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables 2.17 Adding an Index Field2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed; 2.20 Removal of Duplicate Records; 2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3 Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know the Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields 3.8 Binning Based on Predictive Value3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables; 3.11 Using EDA to Investigate Correlated Predictor Variables; 3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4 Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.4.1 The Eigenvalue Criterion; 4.4.2 The Proportion of Variance Explained Criterion; 4.4.3 The Minimum Communality Criterion 4.4.4 The Scree Plot Criterion4.5 Profiling the Principal Components; 4.6 Communalities; 4.6.1 Minimum Communality Criterion; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites; 4.12 An Example of a User-Defined Composite; The R Zone; R References; Exercises; Part II Statistical Analysis; Chapter 5 Univariate Statistical Analysis; 5.1 Data Mining Tasks in Discovering Knowledge in Data; 5.2 Statistical Approaches to Estimation and Prediction; 5.3 Statistical Inference 5.4 How Confident are We in Our Estimates? |
| Record Nr. | UNINA-9910460169903321 |
|
Larose Daniel T.
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||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose
| Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2015 |
| Descrizione fisica | 1 online resource (824 pages) : illustrations |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico |
Data mining
Prediction theory |
| ISBN |
1-118-86867-6
1-118-86870-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Series; Title Page; Copyright; Table of Contents; Dedication; Preface; What is Data Mining? What is Predictive Analytics?; Why is this Book Needed?; Who Will Benefit from this Book?; Danger! Data Mining is Easy to do Badly; "White-Box" Approach; Algorithm Walk-Throughs; Exciting New Topics; The R Zone; Appendix: Data Summarization and Visualization; The Case Study: Bringing it all Together; How the Book is Structured; The Software; Weka: The Open-Source Alternative; The Companion Web Site: www.dataminingconsultant.com; Data Mining and Predictive Analytics as a Textbook; Acknowledgments. Daniel's AcknowledgmentsChantal's Acknowledgments; Part I: Data Preparation; Chapter 1: An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What Is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need For Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.5 Fallacies of Data Mining; 1.6 What Tasks can Data Mining Accomplish; The R Zone; R References; Exercises; Chapter 2: Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data. 2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed. 2.20 Removal of Duplicate Records2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3: Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know The Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables. 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4: Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.5 Profiling the Principal Components; 4.6 Communalities; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites. |
| Record Nr. | UNINA-9910795970103321 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose
| Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2015 |
| Descrizione fisica | 1 online resource (824 pages) : illustrations |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico |
Data mining
Prediction theory |
| ISBN |
1-118-86867-6
1-118-86870-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Series; Title Page; Copyright; Table of Contents; Dedication; Preface; What is Data Mining? What is Predictive Analytics?; Why is this Book Needed?; Who Will Benefit from this Book?; Danger! Data Mining is Easy to do Badly; "White-Box" Approach; Algorithm Walk-Throughs; Exciting New Topics; The R Zone; Appendix: Data Summarization and Visualization; The Case Study: Bringing it all Together; How the Book is Structured; The Software; Weka: The Open-Source Alternative; The Companion Web Site: www.dataminingconsultant.com; Data Mining and Predictive Analytics as a Textbook; Acknowledgments. Daniel's AcknowledgmentsChantal's Acknowledgments; Part I: Data Preparation; Chapter 1: An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What Is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need For Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.5 Fallacies of Data Mining; 1.6 What Tasks can Data Mining Accomplish; The R Zone; R References; Exercises; Chapter 2: Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data. 2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed. 2.20 Removal of Duplicate Records2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3: Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know The Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables. 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4: Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.5 Profiling the Principal Components; 4.6 Communalities; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites. |
| Record Nr. | UNINA-9910807887903321 |
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Larose Daniel T.
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| Hoboken, New Jersey : , : John Wiley & Sons, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Doubly stochastic Poisson processes / / Jan Grandell
| Doubly stochastic Poisson processes / / Jan Grandell |
| Autore | Grandell Jan <1943-> |
| Edizione | [1st ed. 1976.] |
| Pubbl/distr/stampa | Berlin ; ; Heidelberg ; ; New York : , : Springer-Verlag, , [1976] |
| Descrizione fisica | 1 online resource (XII, 240 p.) |
| Disciplina | 519.23 |
| Collana | Lecture notes in mathematics |
| Soggetto topico |
Poisson processes, Doubly stochastic
Measure theory Prediction theory |
| ISBN | 3-540-38258-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Definitions and basic properties -- Some miscellaneous results -- Characterization and convergence of non-atomic random measures -- Limit theorems -- Estimation of random variables -- Linear estimation of random variables in stationary doubly stochastic Poisson sequences -- Estimation of second order properties of stationary doubly stochastic Poisson sequences. |
| Record Nr. | UNISA-996466502103316 |
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Grandell Jan <1943->
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| Berlin ; ; Heidelberg ; ; New York : , : Springer-Verlag, , [1976] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Doubly stochastic Poisson processes / Jan Grandell
| Doubly stochastic Poisson processes / Jan Grandell |
| Autore | Grandell, Jan |
| Pubbl/distr/stampa | Berlin : Springer-Verlag, 1976 |
| Descrizione fisica | x, 234 p. : ill. ; 25 cm |
| Disciplina | 519.23 |
| Collana | Lecture notes in mathematics, 0075-8434 ; 529 |
| Soggetto topico |
Doubly stochastic
Measure theory Point processes Poisson processes Prediction theory Spectral analysis |
| ISBN | 3540077952 |
| Classificazione |
AMS 60G55
AMS 62M15 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991000835749707536 |
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Grandell, Jan
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| Berlin : Springer-Verlag, 1976 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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Faut-il avoir peur de 2030
| Faut-il avoir peur de 2030 |
| Autore | Dainville Alain Oudot de |
| Pubbl/distr/stampa | [Place of publication not identified], : L'Harmattan, 2014 |
| Collana | Diplomatie et stratâegie Faut-il avoir peur de 2030? |
| Soggetto topico |
World politics - 21st century - Forecasting
Strategic forces - Forecasting International relations - Forecasting International relations - History Prediction theory Military & Naval Science Law, Politics & Government Armies |
| ISBN | 2-336-69025-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | fre |
| Record Nr. | UNINA-9910150268403321 |
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Dainville Alain Oudot de
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| [Place of publication not identified], : L'Harmattan, 2014 | ||
| 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 |
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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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