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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 |
| 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 [[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-9910841415103321 |
|
Pankratz Alan <1944->
|
||
| New York, : John Wiley & Sons, 1991 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting with univariate Box-Jenkins models [[electronic resource] ] : concepts and cases / / Alan Pankratz
| Forecasting with univariate Box-Jenkins models [[electronic resource] ] : concepts and cases / / Alan Pankratz |
| Autore | Pankratz Alan <1944-> |
| Pubbl/distr/stampa | New York, : Wiley, c1983 |
| Descrizione fisica | 1 online resource (587 p.) |
| Disciplina |
519.54
519.55 |
| Collana | Wiley series in probability and mathematical statistics. Probability and mathematical statistics. |
| Soggetto topico |
Time-series analysis
Prediction theory |
| ISBN |
1-282-30785-1
9786612307850 0-470-31656-X 0-470-31727-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Forecasting With Univariate Box- Jenkins Models CONCEPTS AND CASES; CONTENTS; PART I. BASIC CONCEPTS; 1 Overview; 1.1 Planning and forecasting; 1.2 What this book is about; 1.3 Time-series data; 1.4 Single-series (univariate) analysis; 1.5 When may UBJ models be used?; 1.6 The Box-Jenkins modeling procedure; 1.7 UBJ models compared with other models; Summary; Questions and problems; 2 Introduction to Box-Jenkins analysis of a single data series; 2.1 Differencing; 2.2 Deviations from the mean
2.3 Two analytical tools: the estimated autocorrelation function (acf) and estimated partial autocorrelation function (pacf)Summary; Questions and problems; 3 Underlying statistical principles; 3.1 Process, realization, and model; 3.2 Two common processes; 3.3 Statistical inference at the identification stage; Summary; Appendix 3 A: Expected value rules and definitions; Questions and problems; 4 An introduction to the practice of ARIMA modeling; 4.1 What is a good model?; 4.2 Two examples of UBJ-ARIMA modeling; Summary; Questions and problems; 5 Notation and the interpretation of ARIMA models 5.1 Three processes and ARIMA (p,d,q) notation5.2 Backshift notation; 5.3 Interpreting ARIMA models I: optimal extrapolation of past values of a single series; 5.4 Interpreting ARIMA models II: rationalizing them from their context; 5.5 Interpreting ARIMA models III: ARIMA(O,d,q) models as exponentially weighted moving averages; Summary; Questions and problems; 6 Identification: stationary models; 6.1 Theoretical acfs and pacf's for five common processes; 6.2 Stationarity; 6.3 Invertibility; 6.4 Deriving theoretical acf's for the MA(1) process 6.5 Deriving theoretical acf's for the AR(1) processSummary; Appendix 6A: The formal conditions for stationarity and invertibility; Appendix 6B Invertibility, uniqueness,and forecast performance; Questions and problems; 7 Identification: nonstationary models; 7.1 Nonstationary mean; 7.2 Nonstationary variance; 7.3 Differencing and deterministic trends; Summary; Appendix 7A: Integration; 8 Estimation; 8.1 Principles of estimation; 8.2 Nonlinear least-squares estimation; 8.3 Estimation-stage results: have we found a good model?; Summary; Appendix 8A: Marquardt's compromise; 8A.1 Overview 8A.2 Application to an MA(1)Appendix 8B: Backcasting; 8B.1 Conditional least squares; 8B.2 Unconditional least squares; 9 Diagnostic checking; 9.1 Are the random shocks independent?; 9.2 Other diagnostic checks; 9.3 Reformulating a model; Summary; Questions and problems; 10 Forecasting; 10.1 The algebra of ARIMA forecasts; 10.2 The dispersion of ARIMA forecasts; 10.3 Forecasting from data in logarithmic form; 10.4 The optimality of ARIMA forecasts; Summary; Appendix 10A:The complementarity of ARIMA models and econometric models; Questions and problems; 11 Seasonal and other periodic models 11.1 Periodic data |
| Record Nr. | UNINA-9910144694203321 |
|
Pankratz Alan <1944->
|
||
| New York, : Wiley, c1983 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting with univariate Box-Jenkins models [[electronic resource] ] : concepts and cases / / Alan Pankratz
| Forecasting with univariate Box-Jenkins models [[electronic resource] ] : concepts and cases / / Alan Pankratz |
| Autore | Pankratz Alan <1944-> |
| Pubbl/distr/stampa | New York, : Wiley, c1983 |
| Descrizione fisica | 1 online resource (587 p.) |
| Disciplina |
519.54
519.55 |
| Collana | Wiley series in probability and mathematical statistics. Probability and mathematical statistics. |
| Soggetto topico |
Time-series analysis
Prediction theory |
| ISBN |
1-282-30785-1
9786612307850 0-470-31656-X 0-470-31727-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Forecasting With Univariate Box- Jenkins Models CONCEPTS AND CASES; CONTENTS; PART I. BASIC CONCEPTS; 1 Overview; 1.1 Planning and forecasting; 1.2 What this book is about; 1.3 Time-series data; 1.4 Single-series (univariate) analysis; 1.5 When may UBJ models be used?; 1.6 The Box-Jenkins modeling procedure; 1.7 UBJ models compared with other models; Summary; Questions and problems; 2 Introduction to Box-Jenkins analysis of a single data series; 2.1 Differencing; 2.2 Deviations from the mean
2.3 Two analytical tools: the estimated autocorrelation function (acf) and estimated partial autocorrelation function (pacf)Summary; Questions and problems; 3 Underlying statistical principles; 3.1 Process, realization, and model; 3.2 Two common processes; 3.3 Statistical inference at the identification stage; Summary; Appendix 3 A: Expected value rules and definitions; Questions and problems; 4 An introduction to the practice of ARIMA modeling; 4.1 What is a good model?; 4.2 Two examples of UBJ-ARIMA modeling; Summary; Questions and problems; 5 Notation and the interpretation of ARIMA models 5.1 Three processes and ARIMA (p,d,q) notation5.2 Backshift notation; 5.3 Interpreting ARIMA models I: optimal extrapolation of past values of a single series; 5.4 Interpreting ARIMA models II: rationalizing them from their context; 5.5 Interpreting ARIMA models III: ARIMA(O,d,q) models as exponentially weighted moving averages; Summary; Questions and problems; 6 Identification: stationary models; 6.1 Theoretical acfs and pacf's for five common processes; 6.2 Stationarity; 6.3 Invertibility; 6.4 Deriving theoretical acf's for the MA(1) process 6.5 Deriving theoretical acf's for the AR(1) processSummary; Appendix 6A: The formal conditions for stationarity and invertibility; Appendix 6B Invertibility, uniqueness,and forecast performance; Questions and problems; 7 Identification: nonstationary models; 7.1 Nonstationary mean; 7.2 Nonstationary variance; 7.3 Differencing and deterministic trends; Summary; Appendix 7A: Integration; 8 Estimation; 8.1 Principles of estimation; 8.2 Nonlinear least-squares estimation; 8.3 Estimation-stage results: have we found a good model?; Summary; Appendix 8A: Marquardt's compromise; 8A.1 Overview 8A.2 Application to an MA(1)Appendix 8B: Backcasting; 8B.1 Conditional least squares; 8B.2 Unconditional least squares; 9 Diagnostic checking; 9.1 Are the random shocks independent?; 9.2 Other diagnostic checks; 9.3 Reformulating a model; Summary; Questions and problems; 10 Forecasting; 10.1 The algebra of ARIMA forecasts; 10.2 The dispersion of ARIMA forecasts; 10.3 Forecasting from data in logarithmic form; 10.4 The optimality of ARIMA forecasts; Summary; Appendix 10A:The complementarity of ARIMA models and econometric models; Questions and problems; 11 Seasonal and other periodic models 11.1 Periodic data |
| Record Nr. | UNINA-9910830016403321 |
|
Pankratz Alan <1944->
|
||
| New York, : Wiley, c1983 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Forecasting with univariate Box-Jenkins models [[electronic resource] ] : concepts and cases / / Alan Pankratz
| Forecasting with univariate Box-Jenkins models [[electronic resource] ] : concepts and cases / / Alan Pankratz |
| Autore | Pankratz Alan <1944-> |
| Pubbl/distr/stampa | New York, : Wiley, c1983 |
| Descrizione fisica | 1 online resource (587 p.) |
| Disciplina |
519.54
519.55 |
| Collana | Wiley series in probability and mathematical statistics. Probability and mathematical statistics. |
| Soggetto topico |
Time-series analysis
Prediction theory |
| ISBN |
1-282-30785-1
9786612307850 0-470-31656-X 0-470-31727-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Forecasting With Univariate Box- Jenkins Models CONCEPTS AND CASES; CONTENTS; PART I. BASIC CONCEPTS; 1 Overview; 1.1 Planning and forecasting; 1.2 What this book is about; 1.3 Time-series data; 1.4 Single-series (univariate) analysis; 1.5 When may UBJ models be used?; 1.6 The Box-Jenkins modeling procedure; 1.7 UBJ models compared with other models; Summary; Questions and problems; 2 Introduction to Box-Jenkins analysis of a single data series; 2.1 Differencing; 2.2 Deviations from the mean
2.3 Two analytical tools: the estimated autocorrelation function (acf) and estimated partial autocorrelation function (pacf)Summary; Questions and problems; 3 Underlying statistical principles; 3.1 Process, realization, and model; 3.2 Two common processes; 3.3 Statistical inference at the identification stage; Summary; Appendix 3 A: Expected value rules and definitions; Questions and problems; 4 An introduction to the practice of ARIMA modeling; 4.1 What is a good model?; 4.2 Two examples of UBJ-ARIMA modeling; Summary; Questions and problems; 5 Notation and the interpretation of ARIMA models 5.1 Three processes and ARIMA (p,d,q) notation5.2 Backshift notation; 5.3 Interpreting ARIMA models I: optimal extrapolation of past values of a single series; 5.4 Interpreting ARIMA models II: rationalizing them from their context; 5.5 Interpreting ARIMA models III: ARIMA(O,d,q) models as exponentially weighted moving averages; Summary; Questions and problems; 6 Identification: stationary models; 6.1 Theoretical acfs and pacf's for five common processes; 6.2 Stationarity; 6.3 Invertibility; 6.4 Deriving theoretical acf's for the MA(1) process 6.5 Deriving theoretical acf's for the AR(1) processSummary; Appendix 6A: The formal conditions for stationarity and invertibility; Appendix 6B Invertibility, uniqueness,and forecast performance; Questions and problems; 7 Identification: nonstationary models; 7.1 Nonstationary mean; 7.2 Nonstationary variance; 7.3 Differencing and deterministic trends; Summary; Appendix 7A: Integration; 8 Estimation; 8.1 Principles of estimation; 8.2 Nonlinear least-squares estimation; 8.3 Estimation-stage results: have we found a good model?; Summary; Appendix 8A: Marquardt's compromise; 8A.1 Overview 8A.2 Application to an MA(1)Appendix 8B: Backcasting; 8B.1 Conditional least squares; 8B.2 Unconditional least squares; 9 Diagnostic checking; 9.1 Are the random shocks independent?; 9.2 Other diagnostic checks; 9.3 Reformulating a model; Summary; Questions and problems; 10 Forecasting; 10.1 The algebra of ARIMA forecasts; 10.2 The dispersion of ARIMA forecasts; 10.3 Forecasting from data in logarithmic form; 10.4 The optimality of ARIMA forecasts; Summary; Appendix 10A:The complementarity of ARIMA models and econometric models; Questions and problems; 11 Seasonal and other periodic models 11.1 Periodic data |
| Record Nr. | UNINA-9910841773903321 |
|
Pankratz Alan <1944->
|
||
| New York, : Wiley, c1983 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Impulsive differential inclusions : a fixed point approach / / by John R. Graef, Johnny Henderson, Abdelghani Ouahab
| Impulsive differential inclusions : a fixed point approach / / by John R. Graef, Johnny Henderson, Abdelghani Ouahab |
| Autore | Graef John R. <1942-> |
| Pubbl/distr/stampa | Berlin ; ; Boston : , : Walter de Gruyter GmbH & Co., KG, , [2013] |
| Descrizione fisica | 1 online resource (412 p.) |
| Disciplina | 515/.352 |
| Altri autori (Persone) |
HendersonJohnny
OuahabAbdelghani |
| Collana | De Gruyter Series in Nonlinear Analysis and Applications |
| Soggetto topico |
Boundary value problems
Differential equations Prediction theory Stochastic processes |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-11-029531-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Contents -- Notations -- Chapter 1. Introduction and Motivations -- Chapter 2. Preliminaries -- Chapter 3. FDEs with Infinite Delay -- Chapter 4. Boundary Value Problems on Infinite Intervals -- Chapter 5. Differential Inclusions -- Chapter 6. Differential Inclusions with Infinite Delay -- Chapter 7. Impulsive FDEs with Variable Times -- Chapter 8. Neutral Differential Inclusions -- Chapter 9. Topology and Geometry of Solution Sets -- Chapter 10. Impulsive Semilinear Differential Inclusions -- Chapter 11. Selected Topics -- Appendix -- Bibliography -- Index |
| Record Nr. | UNINA-9910462706403321 |
|
Graef John R. <1942->
|
||
| Berlin ; ; Boston : , : Walter de Gruyter GmbH & Co., KG, , [2013] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Impulsive differential inclusions : a fixed point approach / / by John R. Graef, Johnny Henderson, Abdelghani Ouahab
| Impulsive differential inclusions : a fixed point approach / / by John R. Graef, Johnny Henderson, Abdelghani Ouahab |
| Autore | Graef John R. <1942-> |
| Pubbl/distr/stampa | Berlin ; ; Boston : , : Walter de Gruyter GmbH & Co., KG, , [2013] |
| Descrizione fisica | 1 online resource (412 p.) |
| Disciplina | 515/.352 |
| Altri autori (Persone) |
HendersonJohnny
OuahabAbdelghani |
| Collana | De Gruyter Series in Nonlinear Analysis and Applications |
| Soggetto topico |
Boundary value problems
Differential equations Prediction theory Stochastic processes |
| Soggetto non controllato |
Boundary Value Problem
Condensing Contraction Controllability Differential Inclusion Filippov's Theorem Hyperbolic Differential Inclusion Impulsive Functional Differential Equation Infinite Delay Normal Cone Relaxation Seeping Process Stability Stochastic Differential Equation Variable Times Viable Solution |
| ISBN | 3-11-029531-8 |
| Classificazione | SK 520 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Contents -- Notations -- Chapter 1. Introduction and Motivations -- Chapter 2. Preliminaries -- Chapter 3. FDEs with Infinite Delay -- Chapter 4. Boundary Value Problems on Infinite Intervals -- Chapter 5. Differential Inclusions -- Chapter 6. Differential Inclusions with Infinite Delay -- Chapter 7. Impulsive FDEs with Variable Times -- Chapter 8. Neutral Differential Inclusions -- Chapter 9. Topology and Geometry of Solution Sets -- Chapter 10. Impulsive Semilinear Differential Inclusions -- Chapter 11. Selected Topics -- Appendix -- Bibliography -- Index |
| Record Nr. | UNINA-9910787646503321 |
|
Graef John R. <1942->
|
||
| Berlin ; ; Boston : , : Walter de Gruyter GmbH & Co., KG, , [2013] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Impulsive differential inclusions : a fixed point approach / / by John R. Graef, Johnny Henderson, Abdelghani Ouahab
| Impulsive differential inclusions : a fixed point approach / / by John R. Graef, Johnny Henderson, Abdelghani Ouahab |
| Autore | Graef John R. <1942-> |
| Pubbl/distr/stampa | Berlin ; ; Boston : , : Walter de Gruyter GmbH & Co., KG, , [2013] |
| Descrizione fisica | 1 online resource (412 p.) |
| Disciplina | 515/.352 |
| Altri autori (Persone) |
HendersonJohnny
OuahabAbdelghani |
| Collana | De Gruyter Series in Nonlinear Analysis and Applications |
| Soggetto topico |
Boundary value problems
Differential equations Prediction theory Stochastic processes |
| Soggetto non controllato |
Boundary Value Problem
Condensing Contraction Controllability Differential Inclusion Filippov's Theorem Hyperbolic Differential Inclusion Impulsive Functional Differential Equation Infinite Delay Normal Cone Relaxation Seeping Process Stability Stochastic Differential Equation Variable Times Viable Solution |
| ISBN | 3-11-029531-8 |
| Classificazione | SK 520 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front matter -- Contents -- Notations -- Chapter 1. Introduction and Motivations -- Chapter 2. Preliminaries -- Chapter 3. FDEs with Infinite Delay -- Chapter 4. Boundary Value Problems on Infinite Intervals -- Chapter 5. Differential Inclusions -- Chapter 6. Differential Inclusions with Infinite Delay -- Chapter 7. Impulsive FDEs with Variable Times -- Chapter 8. Neutral Differential Inclusions -- Chapter 9. Topology and Geometry of Solution Sets -- Chapter 10. Impulsive Semilinear Differential Inclusions -- Chapter 11. Selected Topics -- Appendix -- Bibliography -- Index |
| Record Nr. | UNINA-9910823914003321 |
|
Graef John R. <1942->
|
||
| Berlin ; ; Boston : , : Walter de Gruyter GmbH & Co., KG, , [2013] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inference and prediction in large dimensions [[electronic resource] /] / Denis Bosq, Delphine Blanke
| Inference and prediction in large dimensions [[electronic resource] /] / Denis Bosq, Delphine Blanke |
| Autore | Bosq Denis <1939-> |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley/Dunod, c2007 |
| Descrizione fisica | 1 online resource (338 p.) |
| Disciplina |
519.5/44
519.54 |
| Altri autori (Persone) | BlankeDelphine |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Estimation theory
Nonparametric statistics Stochastic processes Prediction theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-12309-2
9786612123092 0-470-72403-X 0-470-72402-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Inference and Prediction in Large Dimensions; Contents; List of abbreviations; Introduction; Part I Statistical Prediction Theory; 1 Statistical prediction; 1.1 Filtering; 1.2 Some examples; 1.3 The prediction model; 1.4 P-sufficient statistics; 1.5 Optimal predictors; 1.6 Efficient predictors; 1.7 Loss functions and empirical predictors; 1.7.1 Loss function; 1.7.2 Location parameters; 1.7.3 Bayesian predictors; 1.7.4 Linear predictors; 1.8 Multidimensional prediction; Notes; 2 Asymptotic prediction; 2.1 Introduction; 2.2 The basic problem; 2.3 Parametric prediction for stochastic processes
2.4 Predicting some common processes2.5 Equivalent risks; 2.6 Prediction for small time lags; 2.7 Prediction for large time lags; Notes; Part II Inference by Projection; 3 Estimation by adaptive projection; 3.1 Introduction; 3.2 A class of functional parameters; 3.3 Oracle; 3.4 Parametric rate; 3.5 Nonparametric rates; 3.6 Rate in uniform norm; 3.7 Adaptive projection; 3.7.1 Behaviour of truncation index; 3.7.2 Superoptimal rate; 3.7.3 The general case; 3.7.4 Discussion and implementation; 3.8 Adaptive estimation in continuous time; Notes; 4 Functional tests of fit 4.1 Generalized chi-square tests4.2 Tests based on linear estimators; 4.2.1 Consistency of the test; 4.2.2 Application; 4.3 Efficiency of functional tests of fit; 4.3.1 Adjacent hypotheses; 4.3.2 Bahadur efficiency; 4.4 Tests based on the uniform norm; 4.5 Extensions. Testing regression; 4.6 Functional tests for stochastic processes; Notes; 5 Prediction by projection; 5.1 A class of nonparametric predictors; 5.2 Guilbart spaces; 5.3 Predicting the conditional distribution; 5.4 Predicting the conditional distribution function; Notes; Part III Inference by Kernels 6 Kernel method in discrete time6.1 Presentation of the method; 6.2 Kernel estimation in the i.i.d. case; 6.3 Density estimation in the dependent case; 6.3.1 Mean-square error and asymptotic normality; 6.3.2 Almost sure convergence; 6.4 Regression estimation in the dependent case; 6.4.1 Framework and notations; 6.4.2 Pointwise convergence; 6.4.3 Uniform convergence; 6.5 Nonparametric prediction by kernel; 6.5.1 Prediction for a stationary Markov process of order k; 6.5.2 Prediction for general processes; Notes; 7 Kernelmethodin continuous time 7.1 Optimal and superoptimal rates for density estimation7.1.1 The optimal framework; 7.1.2 The superoptimal case; 7.2 From optimal to superoptimal rates; 7.2.1 Intermediate rates; 7.2.2 Classes of processes and examples; 7.2.3 Mean-square convergence; 7.2.4 Almost sure convergence; 7.2.5 An adaptive approach; 7.3 Regression estimation; 7.3.1 Pointwise almost sure convergence; 7.3.2 Uniform almost sure convergence; 7.4 Nonparametric prediction by kernel; Notes; 8 Kernel method from sampled data; 8.1 Density estimation; 8.1.1 High rate sampling; 8.1.2 Adequate sampling schemes 8.2 Regression estimation |
| Record Nr. | UNINA-9910144718003321 |
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Bosq Denis <1939->
|
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| Chichester, England ; ; Hoboken, NJ, : John Wiley/Dunod, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Inference and prediction in large dimensions [[electronic resource] /] / Denis Bosq, Delphine Blanke
| Inference and prediction in large dimensions [[electronic resource] /] / Denis Bosq, Delphine Blanke |
| Autore | Bosq Denis <1939-> |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley/Dunod, c2007 |
| Descrizione fisica | 1 online resource (338 p.) |
| Disciplina |
519.5/44
519.54 |
| Altri autori (Persone) | BlankeDelphine |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Estimation theory
Nonparametric statistics Stochastic processes Prediction theory |
| ISBN |
1-282-12309-2
9786612123092 0-470-72403-X 0-470-72402-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Inference and Prediction in Large Dimensions; Contents; List of abbreviations; Introduction; Part I Statistical Prediction Theory; 1 Statistical prediction; 1.1 Filtering; 1.2 Some examples; 1.3 The prediction model; 1.4 P-sufficient statistics; 1.5 Optimal predictors; 1.6 Efficient predictors; 1.7 Loss functions and empirical predictors; 1.7.1 Loss function; 1.7.2 Location parameters; 1.7.3 Bayesian predictors; 1.7.4 Linear predictors; 1.8 Multidimensional prediction; Notes; 2 Asymptotic prediction; 2.1 Introduction; 2.2 The basic problem; 2.3 Parametric prediction for stochastic processes
2.4 Predicting some common processes2.5 Equivalent risks; 2.6 Prediction for small time lags; 2.7 Prediction for large time lags; Notes; Part II Inference by Projection; 3 Estimation by adaptive projection; 3.1 Introduction; 3.2 A class of functional parameters; 3.3 Oracle; 3.4 Parametric rate; 3.5 Nonparametric rates; 3.6 Rate in uniform norm; 3.7 Adaptive projection; 3.7.1 Behaviour of truncation index; 3.7.2 Superoptimal rate; 3.7.3 The general case; 3.7.4 Discussion and implementation; 3.8 Adaptive estimation in continuous time; Notes; 4 Functional tests of fit 4.1 Generalized chi-square tests4.2 Tests based on linear estimators; 4.2.1 Consistency of the test; 4.2.2 Application; 4.3 Efficiency of functional tests of fit; 4.3.1 Adjacent hypotheses; 4.3.2 Bahadur efficiency; 4.4 Tests based on the uniform norm; 4.5 Extensions. Testing regression; 4.6 Functional tests for stochastic processes; Notes; 5 Prediction by projection; 5.1 A class of nonparametric predictors; 5.2 Guilbart spaces; 5.3 Predicting the conditional distribution; 5.4 Predicting the conditional distribution function; Notes; Part III Inference by Kernels 6 Kernel method in discrete time6.1 Presentation of the method; 6.2 Kernel estimation in the i.i.d. case; 6.3 Density estimation in the dependent case; 6.3.1 Mean-square error and asymptotic normality; 6.3.2 Almost sure convergence; 6.4 Regression estimation in the dependent case; 6.4.1 Framework and notations; 6.4.2 Pointwise convergence; 6.4.3 Uniform convergence; 6.5 Nonparametric prediction by kernel; 6.5.1 Prediction for a stationary Markov process of order k; 6.5.2 Prediction for general processes; Notes; 7 Kernelmethodin continuous time 7.1 Optimal and superoptimal rates for density estimation7.1.1 The optimal framework; 7.1.2 The superoptimal case; 7.2 From optimal to superoptimal rates; 7.2.1 Intermediate rates; 7.2.2 Classes of processes and examples; 7.2.3 Mean-square convergence; 7.2.4 Almost sure convergence; 7.2.5 An adaptive approach; 7.3 Regression estimation; 7.3.1 Pointwise almost sure convergence; 7.3.2 Uniform almost sure convergence; 7.4 Nonparametric prediction by kernel; Notes; 8 Kernel method from sampled data; 8.1 Density estimation; 8.1.1 High rate sampling; 8.1.2 Adequate sampling schemes 8.2 Regression estimation |
| Record Nr. | UNINA-9910830304003321 |
|
Bosq Denis <1939->
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley/Dunod, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
