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Advances in Robust Fractional Control [[electronic resource] /] / by Fabrizio Padula, Antonio Visioli
Advances in Robust Fractional Control [[electronic resource] /] / by Fabrizio Padula, Antonio Visioli
Autore Padula Fabrizio
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (182 p.)
Disciplina 519.5/3
629.8
Soggetto topico Control engineering
Chemical engineering
Industrial engineering
Production engineering
Control and Systems Theory
Industrial Chemistry/Chemical Engineering
Industrial and Production Engineering
ISBN 3-319-10930-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to Fractional Calculus -- Fractional Systems for Control -- Fractional Proportional-Integral-Derivative Controllers -- FOPID Controller Additional Functionalities.- H-infinity Control of Fractional Systems -- H-infinity Optimization-based FOPID Design -- Control Design Based on Input-Output Inversion.
Record Nr. UNINA-9910299845803321
Padula Fabrizio  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied longitudinal analysis / / Garrett M. Fitzmaurice, Nan M. Laird, James H. Ware
Applied longitudinal analysis / / Garrett M. Fitzmaurice, Nan M. Laird, James H. Ware
Autore Fitzmaurice Garrett M. <1962->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2011]
Descrizione fisica 1 online resource (1309 p.)
Disciplina 519.5/3
519.53
Collana Wiley series in probability and statistics
Soggetto topico Longitudinal method
Regression analysis
Multivariate analysis
Medical statistics
ISBN 9781119513469
1-119-51346-4
1-118-55179-6
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Half Title page; Title page; Copyright page; Dedication; Preface; Preface to First Edition; Acknowledgments; Part I: Introduction to Longitudinal and Clustered Data; Chapter 1: Longitudinal and Clustered Data; 1.1 Introduction; 1.2 Longitudinal and Clustered Data; 1.3 Examples; 1.4 Regression Models for Correlated Responses; 1.5 Organization of the Book; 1.6 Further Reading; Chapter 2: Longitudinal Data: Basic Concepts; 2.1 Introduction; 2.2 Objectives of Longitudinal Analysis; 2.3 Defining Features of Longitudinal Data; 2.4 Example: Treatment of Lead-Exposed Children Trial
2.5 Sources of Correlation in Longitudinal Data2.6 Further Reading; Part II: Linear Models for Longitudinal Continuous Data; Chapter 3: Overview of Linear Models for Longitudinal Data; 3.1 Introduction; 3.2 Notation and Distributional Assumptions; 3.3 Simple Descriptive Methods of Analysis; 3.4 Modeling the Mean; 3.5 Modeling the Covariance; 3.6 Historical Approaches; 3.7 Further Reading; Chapter 4: Estimation and Statistical Inference; 4.1 Introduction; 4.2 Estimation: Maximum Likelihood; 4.3 Missing Data Issues; 4.4 Statistical Inference; 4.5 Restricted Maximum Likelihood (REML) Estimation
4.6 Further ReadingChapter 5: Modeling the Mean: Analyzing Response Profiles; 5.1 Introduction; 5.2 Hypotheses Concerning Response Profiles; 5.3 General Linear Model Formulation; 5.4 Case Study; 5.5 One-Degree-of-Freedom Tests for Group by Time Interaction; 5.6 Adjustment for Baseline Response; 5.7 Alternative Methods of Adjusting for Baseline Response; 5.8 Strengths and Weaknesses of Analyzing Response Profiles; 5.9 Computing: Analyzing Response Profiles Using PROC MIXED in SAS; 5.10 Further Reading; Chapter 6: Modeling the Mean: Parametric Curves; 6.1 Introduction
6.2 Polynomial Trends in Time6.3 Linear Splines; 6.4 General Linear Model Formulation; 6.5 Case Studies; 6.6 Computing: Fitting Parametric Curves Using PROC MIXED in SAS; 6.7 Further Reading; Chapter 7: Modeling the Covariance; 7.1 Introduction; 7.2 Implications of Correlation among Longitudinal Data; 7.3 Unstructured Covariance; 7.4 Covariance Pattern Models; 7.5 Choice among Covariance Pattern Models; 7.6 Case Study; 7.7 Discussion: Strengths and Weaknesses of Covariance Pattern Models; 7.8 Computing: Fitting Covariance Pattern Models Using PROC MIXED in SAS; 7.9 Further Reading
Chapter 8: Linear Mixed Effects Models8.1 Introduction; 8.2 Linear Mixed Effects Models; 8.3 Random Effects Covariance Structure; 8.4 Two-Stage Random Effects Formulation; 8.5 Choice among Random Effects Covariance Models; 8.6 Prediction of Random Effects; 8.7 Prediction and Shrinkage; 8.8 Case Studies; 8.9 Computing: Fitting Linear Mixed Effects Models Using PROC MIXED in SAS; 8.10 Further Reading; Chapter 9: Fixed Effects versus Random Effects Models; 9.1 Introduction; 9.2 Linear Fixed Effects Models; 9.3 Fixed Effects versus Random Effects: Bias-Variance Trade-off
9.4 Resolving the Dilemma of Choosing Between Fixed and Random Effects Models
Record Nr. UNINA-9910555092603321
Fitzmaurice Garrett M. <1962->  
Hoboken, New Jersey : , : Wiley, , [2011]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied longitudinal analysis / / Garrett M. Fitzmaurice, Nan M. Laird, James H. Ware
Applied longitudinal analysis / / Garrett M. Fitzmaurice, Nan M. Laird, James H. Ware
Autore Fitzmaurice Garrett M. <1962->
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2011]
Descrizione fisica 1 online resource (1309 p.)
Disciplina 519.5/3
519.53
Collana Wiley series in probability and statistics
Soggetto topico Longitudinal method
Regression analysis
Multivariate analysis
Medical statistics
ISBN 1-119-51346-4
1-118-55179-6
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Half Title page; Title page; Copyright page; Dedication; Preface; Preface to First Edition; Acknowledgments; Part I: Introduction to Longitudinal and Clustered Data; Chapter 1: Longitudinal and Clustered Data; 1.1 Introduction; 1.2 Longitudinal and Clustered Data; 1.3 Examples; 1.4 Regression Models for Correlated Responses; 1.5 Organization of the Book; 1.6 Further Reading; Chapter 2: Longitudinal Data: Basic Concepts; 2.1 Introduction; 2.2 Objectives of Longitudinal Analysis; 2.3 Defining Features of Longitudinal Data; 2.4 Example: Treatment of Lead-Exposed Children Trial
2.5 Sources of Correlation in Longitudinal Data2.6 Further Reading; Part II: Linear Models for Longitudinal Continuous Data; Chapter 3: Overview of Linear Models for Longitudinal Data; 3.1 Introduction; 3.2 Notation and Distributional Assumptions; 3.3 Simple Descriptive Methods of Analysis; 3.4 Modeling the Mean; 3.5 Modeling the Covariance; 3.6 Historical Approaches; 3.7 Further Reading; Chapter 4: Estimation and Statistical Inference; 4.1 Introduction; 4.2 Estimation: Maximum Likelihood; 4.3 Missing Data Issues; 4.4 Statistical Inference; 4.5 Restricted Maximum Likelihood (REML) Estimation
4.6 Further ReadingChapter 5: Modeling the Mean: Analyzing Response Profiles; 5.1 Introduction; 5.2 Hypotheses Concerning Response Profiles; 5.3 General Linear Model Formulation; 5.4 Case Study; 5.5 One-Degree-of-Freedom Tests for Group by Time Interaction; 5.6 Adjustment for Baseline Response; 5.7 Alternative Methods of Adjusting for Baseline Response; 5.8 Strengths and Weaknesses of Analyzing Response Profiles; 5.9 Computing: Analyzing Response Profiles Using PROC MIXED in SAS; 5.10 Further Reading; Chapter 6: Modeling the Mean: Parametric Curves; 6.1 Introduction
6.2 Polynomial Trends in Time6.3 Linear Splines; 6.4 General Linear Model Formulation; 6.5 Case Studies; 6.6 Computing: Fitting Parametric Curves Using PROC MIXED in SAS; 6.7 Further Reading; Chapter 7: Modeling the Covariance; 7.1 Introduction; 7.2 Implications of Correlation among Longitudinal Data; 7.3 Unstructured Covariance; 7.4 Covariance Pattern Models; 7.5 Choice among Covariance Pattern Models; 7.6 Case Study; 7.7 Discussion: Strengths and Weaknesses of Covariance Pattern Models; 7.8 Computing: Fitting Covariance Pattern Models Using PROC MIXED in SAS; 7.9 Further Reading
Chapter 8: Linear Mixed Effects Models8.1 Introduction; 8.2 Linear Mixed Effects Models; 8.3 Random Effects Covariance Structure; 8.4 Two-Stage Random Effects Formulation; 8.5 Choice among Random Effects Covariance Models; 8.6 Prediction of Random Effects; 8.7 Prediction and Shrinkage; 8.8 Case Studies; 8.9 Computing: Fitting Linear Mixed Effects Models Using PROC MIXED in SAS; 8.10 Further Reading; Chapter 9: Fixed Effects versus Random Effects Models; 9.1 Introduction; 9.2 Linear Fixed Effects Models; 9.3 Fixed Effects versus Random Effects: Bias-Variance Trade-off
9.4 Resolving the Dilemma of Choosing Between Fixed and Random Effects Models
Record Nr. UNINA-9910830134903321
Fitzmaurice Garrett M. <1962->  
Hoboken, New Jersey : , : Wiley, , [2011]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2016]
Descrizione fisica 1 online resource (763 pages) : illustrations
Disciplina 519.5/3
Soggetto topico Analysis of variance
Multivariate analysis
Soggetto genere / forma Electronic books.
ISBN 1-118-63223-0
1-118-63231-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910460626803321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2016]
Descrizione fisica 1 online resource (763 pages) : illustrations
Disciplina 519.5/3
Soggetto topico Analysis of variance
Multivariate analysis
ISBN 1118632230
9781118632239
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910796096703321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2016]
Descrizione fisica 1 online resource (763 pages) : illustrations
Disciplina 519.5/3
Soggetto topico Analysis of variance
Multivariate analysis
ISBN 1118632230
9781118632239
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910808575603321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Basic and advanced Bayesian structural equation modeling [[electronic resource] ] : with applications in the medical and behavioral sciences / / Sik-Yum Lee and Xin-Yuan Song
Basic and advanced Bayesian structural equation modeling [[electronic resource] ] : with applications in the medical and behavioral sciences / / Sik-Yum Lee and Xin-Yuan Song
Autore Lee Sik-Yum
Pubbl/distr/stampa Hoboken, : Wiley, 2012
Descrizione fisica 1 online resource (397 p.)
Disciplina 519.5/3
Altri autori (Persone) SongXin-Yuan
Collana Wiley series in probability and statistics
Soggetto topico Structural equation modeling
Bayesian statistical decision theory
ISBN 1-118-35887-2
1-280-87995-5
9786613721266
1-118-35880-5
1-118-35888-0
1-118-35943-7
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Basic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram
2.3 SEMs with fixed covariates 2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods
3.5 Bayesian estimation via WinBUGS Appendix 3.1: The gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω]; Appendix 3.4: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics
4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 Lν-measure; 4.4 Illustration; 4.5 Goodness of fit and model checking methods; 4.5.1 Posterior predictive p-value; 4.5.2 Residual analysis; Appendix 4.1: WinBUGS code; Appendix 4.2: R code in Bayes factor example; Appendix 4.3: Posterior predictive p-value for model assessment; References; 5 Practical structural equation models; 5.1 Introduction; 5.2 SEMs with continuous and ordered categorical variables; 5.2.1 Introduction; 5.2.2 The basic model; 5.2.3 Bayesian analysis
5.2.4 Application: Bayesian analysis of quality of life data 5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real example
Appendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables
Record Nr. UNINA-9910141259703321
Lee Sik-Yum  
Hoboken, : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Basic and advanced Bayesian structural equation modeling [[electronic resource] ] : with applications in the medical and behavioral sciences / / Sik-Yum Lee and Xin-Yuan Song
Basic and advanced Bayesian structural equation modeling [[electronic resource] ] : with applications in the medical and behavioral sciences / / Sik-Yum Lee and Xin-Yuan Song
Autore Lee Sik-Yum
Pubbl/distr/stampa Hoboken, : Wiley, 2012
Descrizione fisica 1 online resource (397 p.)
Disciplina 519.5/3
Altri autori (Persone) SongXin-Yuan
Collana Wiley series in probability and statistics
Soggetto topico Structural equation modeling
Bayesian statistical decision theory
ISBN 1-118-35887-2
1-280-87995-5
9786613721266
1-118-35880-5
1-118-35888-0
1-118-35943-7
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Basic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram
2.3 SEMs with fixed covariates 2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods
3.5 Bayesian estimation via WinBUGS Appendix 3.1: The gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω]; Appendix 3.4: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics
4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 Lν-measure; 4.4 Illustration; 4.5 Goodness of fit and model checking methods; 4.5.1 Posterior predictive p-value; 4.5.2 Residual analysis; Appendix 4.1: WinBUGS code; Appendix 4.2: R code in Bayes factor example; Appendix 4.3: Posterior predictive p-value for model assessment; References; 5 Practical structural equation models; 5.1 Introduction; 5.2 SEMs with continuous and ordered categorical variables; 5.2.1 Introduction; 5.2.2 The basic model; 5.2.3 Bayesian analysis
5.2.4 Application: Bayesian analysis of quality of life data 5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real example
Appendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables
Record Nr. UNINA-9910821778903321
Lee Sik-Yum  
Hoboken, : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cluster Analysis / / Brian S. Everitt, Sabine Landau, Morven Leese
Cluster Analysis / / Brian S. Everitt, Sabine Landau, Morven Leese
Autore Everitt Brian
Edizione [5th edition]
Pubbl/distr/stampa Chicester : , : Wiley, , 2010
Descrizione fisica 1 online resource (xii, 330 pages) : illustrations
Disciplina 519.5/3
519.53
Collana Wiley Series in Probability and Statistics
Soggetto topico Cluster analysis
ISBN 1-280-76795-2
9786613678720
1-118-30300-8
0-470-97781-7
0-470-97780-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Matter -- An Introduction to Classification and Clustering -- Detecting Clusters Graphically -- Measurement of Proximity -- Hierarchical Clustering -- Optimization Clustering Techniques -- Finite Mixture Densities as Models for Cluster Analysis -- Model-Based Cluster Analysis for Structured Data -- Miscellaneous Clustering Methods -- Some Final Comments and Guidelines -- Bibliography -- Index.
Record Nr. UNINA-9910140852403321
Everitt Brian  
Chicester : , : Wiley, , 2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cluster effects in mining complex data [[electronic resource] /] / M. Ishaq Bhati
Cluster effects in mining complex data [[electronic resource] /] / M. Ishaq Bhati
Autore Bhatti M. Ishaq
Pubbl/distr/stampa New York, : Nova Science Publisher's, c2012
Descrizione fisica 1 online resource (267 p.)
Disciplina 519.5/3
Collana Mathematics research developments
Soggetto topico Cluster analysis
Data mining
Econometrics
Soggetto genere / forma Electronic books.
ISBN 1-62808-669-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910462368403321
Bhatti M. Ishaq  
New York, : Nova Science Publisher's, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui