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Applications of computer vision in fashion and textiles / / edited by W. K. Wong
| Applications of computer vision in fashion and textiles / / edited by W. K. Wong |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : Woodhead Publishing, , 2017 |
| Descrizione fisica | 1 online resource (314 pages) |
| Disciplina | 006.37 |
| Collana | Textile Institute Book Series |
| Soggetto topico |
Computer vision
Fashion Textile fabrics |
| ISBN |
0-08-101217-9
0-08-101218-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910583494203321 |
| Cambridge, Massachusetts : , : Woodhead Publishing, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied optimal designs [[electronic resource] /] / edited by Martijn P.F. Berger, Weng Kee Wong
| Applied optimal designs [[electronic resource] /] / edited by Martijn P.F. Berger, Weng Kee Wong |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (313 p.) |
| Disciplina |
519.5/7
519.57 |
| Altri autori (Persone) |
BergerMartijn
WongWeng Kee |
| Soggetto topico |
Optimal designs (Statistics)
Experimental design |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-27069-1
9786610270699 0-470-30004-3 0-470-85700-5 0-470-85699-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Optimal Designs; Contents; List of Contributors; Editors' Foreword; 1 Optimal Design in Educational Testing; 1.1 Introduction; 1.1.1 Paper-and-pencil or computerized adaptive testing; 1.1.2 Dichotomous response; 1.1.3 Polytomous response; 1.1.4 Information functions; 1.1.5 Design problems; 1.2 Test Design; 1.2.1 Fixed-form test design; 1.2.2 Test design for CAT; 1.3 Sampling Design; 1.3.1 Paper-and-pencil calibration; 1.3.2 CAT calibration; 1.4 Future Directions; Acknowledgements; References; 2 Optimal On-line Calibration of Testlets; 2.1 Introduction; 2.2 Background
2.2.1 Item response functions2.2.2 D-optimal design criterion; 2.3 Solution for Optimal Designs; 2.3.1 Mathematical programming model; 2.3.2 Unconstrained conjugate-gradient method; 2.3.3 Constrained conjugate-gradient method; 2.3.4 Gradient of log det M(B; Q, x); 2.3.5 MCMC sequential estimation of item parameters; 2.3.6 Note on performance measures; 2.4 Simulation Results; 2.5 Discussion; Appendix A Derivation of the Gradient of log det M(B; Q, x); Appendix B Projection on the Null Space of the Constraint Matrix; Acknowledgements; References 3 On the Empirical Relevance of Optimal Designs for the Measurement of Preferences3.1 Introduction; 3.2 Conjoint Analysis; 3.3 Paired Comparison Models in Conjoint Analysis; 3.4 Design Issues; 3.5 Experiments; 3.5.1 Experiment 1; 3.5.2 Experiment 2; 3.6 Discussion; Acknowledgements; References; 4 Designing Optimal Two-stage Epidemiological Studies; 4.1 Introduction; 4.2 Illustrative Examples; 4.2.1 Example 1; 4.2.2 Example 2; 4.2.3 Example 3; 4.3 Meanscore; 4.3.1 Example of meanscore; 4.4 Optimal Design and Meanscore; 4.4.1 Optimal design derivation for fixed second stage sample size 4.4.2 Optimal design derivation for fixed budget4.4.3 Optimal design derivation for fixed precision; 4.4.4 Computational issues; 4.5 Deriving Optimal Designs in Practice; 4.5.1 Data needed to compute optimal designs; 4.5.2 Examples of optimal design; 4.5.3 The optimal sampling package; 4.5.4 Sensitivity of design to sampling variation in pilot data; 4.6 Summary; 4.7 Appendix 1 Brief Description of Software Used; 4.7.1 R language; 4.7.2 S-PLUS; 4.7.3 STATA; 4.8 Appendix 2 The Optimal Sampling Package; 4.8.1 Illustrative data sets; 4.9 Appendix 3 Using the Optimal Package in R 4.9.1 Syntax and features of optimal sampling command 'budget' in R4.9.2 Example; 4.10 Appendix 4 Using the Optimal Package in S-Plus; 4.11 Appendix 5 Using the Optimal Package in STATA; 4.11.1 Syntax and features of 'optbud' function in STATA; 4.11.2 Analysis with categorical variables; 4.11.3 Illustrative example; References; 5 Response-Driven Designs in Drug Development; 5.1 Introduction; 5.2 Motivating Example: Quantal Models for Dose Response; 5.2.1 Optimality criteria; 5.3 Continuous Models; 5.3.1 Example 3.1; 5.3.2 Example 3.2 5.4 Variance Depending on Unknown Parameters and Multi-response Models |
| Record Nr. | UNINA-9910143748103321 |
| Hoboken, NJ, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied optimal designs [[electronic resource] /] / edited by Martijn P.F. Berger, Weng Kee Wong
| Applied optimal designs [[electronic resource] /] / edited by Martijn P.F. Berger, Weng Kee Wong |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (313 p.) |
| Disciplina |
519.5/7
519.57 |
| Altri autori (Persone) |
BergerMartijn
WongWeng Kee |
| Soggetto topico |
Optimal designs (Statistics)
Experimental design |
| ISBN |
1-280-27069-1
9786610270699 0-470-30004-3 0-470-85700-5 0-470-85699-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Optimal Designs; Contents; List of Contributors; Editors' Foreword; 1 Optimal Design in Educational Testing; 1.1 Introduction; 1.1.1 Paper-and-pencil or computerized adaptive testing; 1.1.2 Dichotomous response; 1.1.3 Polytomous response; 1.1.4 Information functions; 1.1.5 Design problems; 1.2 Test Design; 1.2.1 Fixed-form test design; 1.2.2 Test design for CAT; 1.3 Sampling Design; 1.3.1 Paper-and-pencil calibration; 1.3.2 CAT calibration; 1.4 Future Directions; Acknowledgements; References; 2 Optimal On-line Calibration of Testlets; 2.1 Introduction; 2.2 Background
2.2.1 Item response functions2.2.2 D-optimal design criterion; 2.3 Solution for Optimal Designs; 2.3.1 Mathematical programming model; 2.3.2 Unconstrained conjugate-gradient method; 2.3.3 Constrained conjugate-gradient method; 2.3.4 Gradient of log det M(B; Q, x); 2.3.5 MCMC sequential estimation of item parameters; 2.3.6 Note on performance measures; 2.4 Simulation Results; 2.5 Discussion; Appendix A Derivation of the Gradient of log det M(B; Q, x); Appendix B Projection on the Null Space of the Constraint Matrix; Acknowledgements; References 3 On the Empirical Relevance of Optimal Designs for the Measurement of Preferences3.1 Introduction; 3.2 Conjoint Analysis; 3.3 Paired Comparison Models in Conjoint Analysis; 3.4 Design Issues; 3.5 Experiments; 3.5.1 Experiment 1; 3.5.2 Experiment 2; 3.6 Discussion; Acknowledgements; References; 4 Designing Optimal Two-stage Epidemiological Studies; 4.1 Introduction; 4.2 Illustrative Examples; 4.2.1 Example 1; 4.2.2 Example 2; 4.2.3 Example 3; 4.3 Meanscore; 4.3.1 Example of meanscore; 4.4 Optimal Design and Meanscore; 4.4.1 Optimal design derivation for fixed second stage sample size 4.4.2 Optimal design derivation for fixed budget4.4.3 Optimal design derivation for fixed precision; 4.4.4 Computational issues; 4.5 Deriving Optimal Designs in Practice; 4.5.1 Data needed to compute optimal designs; 4.5.2 Examples of optimal design; 4.5.3 The optimal sampling package; 4.5.4 Sensitivity of design to sampling variation in pilot data; 4.6 Summary; 4.7 Appendix 1 Brief Description of Software Used; 4.7.1 R language; 4.7.2 S-PLUS; 4.7.3 STATA; 4.8 Appendix 2 The Optimal Sampling Package; 4.8.1 Illustrative data sets; 4.9 Appendix 3 Using the Optimal Package in R 4.9.1 Syntax and features of optimal sampling command 'budget' in R4.9.2 Example; 4.10 Appendix 4 Using the Optimal Package in S-Plus; 4.11 Appendix 5 Using the Optimal Package in STATA; 4.11.1 Syntax and features of 'optbud' function in STATA; 4.11.2 Analysis with categorical variables; 4.11.3 Illustrative example; References; 5 Response-Driven Designs in Drug Development; 5.1 Introduction; 5.2 Motivating Example: Quantal Models for Dose Response; 5.2.1 Optimality criteria; 5.3 Continuous Models; 5.3.1 Example 3.1; 5.3.2 Example 3.2 5.4 Variance Depending on Unknown Parameters and Multi-response Models |
| Record Nr. | UNINA-9910829983303321 |
| Hoboken, NJ, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied optimal designs / / edited by Martijn P.F. Berger, Weng Kee Wong
| Applied optimal designs / / edited by Martijn P.F. Berger, Weng Kee Wong |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (313 p.) |
| Disciplina | 519.5/7 |
| Altri autori (Persone) |
BergerMartijn
WongWeng Kee |
| Soggetto topico |
Optimal designs (Statistics)
Experimental design |
| ISBN |
1-280-27069-1
9786610270699 0-470-30004-3 0-470-85700-5 0-470-85699-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied Optimal Designs; Contents; List of Contributors; Editors' Foreword; 1 Optimal Design in Educational Testing; 1.1 Introduction; 1.1.1 Paper-and-pencil or computerized adaptive testing; 1.1.2 Dichotomous response; 1.1.3 Polytomous response; 1.1.4 Information functions; 1.1.5 Design problems; 1.2 Test Design; 1.2.1 Fixed-form test design; 1.2.2 Test design for CAT; 1.3 Sampling Design; 1.3.1 Paper-and-pencil calibration; 1.3.2 CAT calibration; 1.4 Future Directions; Acknowledgements; References; 2 Optimal On-line Calibration of Testlets; 2.1 Introduction; 2.2 Background
2.2.1 Item response functions2.2.2 D-optimal design criterion; 2.3 Solution for Optimal Designs; 2.3.1 Mathematical programming model; 2.3.2 Unconstrained conjugate-gradient method; 2.3.3 Constrained conjugate-gradient method; 2.3.4 Gradient of log det M(B; Q, x); 2.3.5 MCMC sequential estimation of item parameters; 2.3.6 Note on performance measures; 2.4 Simulation Results; 2.5 Discussion; Appendix A Derivation of the Gradient of log det M(B; Q, x); Appendix B Projection on the Null Space of the Constraint Matrix; Acknowledgements; References 3 On the Empirical Relevance of Optimal Designs for the Measurement of Preferences3.1 Introduction; 3.2 Conjoint Analysis; 3.3 Paired Comparison Models in Conjoint Analysis; 3.4 Design Issues; 3.5 Experiments; 3.5.1 Experiment 1; 3.5.2 Experiment 2; 3.6 Discussion; Acknowledgements; References; 4 Designing Optimal Two-stage Epidemiological Studies; 4.1 Introduction; 4.2 Illustrative Examples; 4.2.1 Example 1; 4.2.2 Example 2; 4.2.3 Example 3; 4.3 Meanscore; 4.3.1 Example of meanscore; 4.4 Optimal Design and Meanscore; 4.4.1 Optimal design derivation for fixed second stage sample size 4.4.2 Optimal design derivation for fixed budget4.4.3 Optimal design derivation for fixed precision; 4.4.4 Computational issues; 4.5 Deriving Optimal Designs in Practice; 4.5.1 Data needed to compute optimal designs; 4.5.2 Examples of optimal design; 4.5.3 The optimal sampling package; 4.5.4 Sensitivity of design to sampling variation in pilot data; 4.6 Summary; 4.7 Appendix 1 Brief Description of Software Used; 4.7.1 R language; 4.7.2 S-PLUS; 4.7.3 STATA; 4.8 Appendix 2 The Optimal Sampling Package; 4.8.1 Illustrative data sets; 4.9 Appendix 3 Using the Optimal Package in R 4.9.1 Syntax and features of optimal sampling command 'budget' in R4.9.2 Example; 4.10 Appendix 4 Using the Optimal Package in S-Plus; 4.11 Appendix 5 Using the Optimal Package in STATA; 4.11.1 Syntax and features of 'optbud' function in STATA; 4.11.2 Analysis with categorical variables; 4.11.3 Illustrative example; References; 5 Response-Driven Designs in Drug Development; 5.1 Introduction; 5.2 Motivating Example: Quantal Models for Dose Response; 5.2.1 Optimality criteria; 5.3 Continuous Models; 5.3.1 Example 3.1; 5.3.2 Example 3.2 5.4 Variance Depending on Unknown Parameters and Multi-response Models |
| Record Nr. | UNINA-9910876581203321 |
| Hoboken, NJ, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to optimal designs for social and biomedical research [[electronic resource] /] / Martijn P.F. Berger, Weng Kee Wong
| An introduction to optimal designs for social and biomedical research [[electronic resource] /] / Martijn P.F. Berger, Weng Kee Wong |
| Autore | Berger Martijn P. F |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (348 p.) |
| Disciplina | 300.72 |
| Altri autori (Persone) | WongWeng Kee |
| Collana | Statistics in Practice |
| Soggetto topico |
Social sciences - Research
Biology - Research Optimal designs (Statistics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-18889-5
9786612188893 0-470-74691-2 0-470-74692-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
An Introduction to Optimal Designs for Social and Biomedical Research; Contents; Preface; Acknowledgements; 1 Introduction to designs; 1.1 Introduction; 1.2 Stages of the research process; 1.2.1 Choice of a 'good' design; 1.3 Research design; 1.3.1 Choice of independent variables and levels; 1.3.2 Units of analysis; 1.3.3 Variables; 1.3.4 Replication; 1.4 Types of research designs; 1.5 Requirements for a 'good' design; 1.5.1 Statistical conclusion validity; 1.5.2 Internal validity; 1.5.3 Control of (unwanted) variation; 1.6 Ethical aspects of design choice
1.7 Exact versus approximate designs1.8 Examples; 1.8.1 Radiation dosage example; 1.8.2 Designs for the Poggendorff and Ponzo illusion experiments; 1.8.3 Uncertainty about best .tting regression models; 1.8.4 Designs for a priori contrasts among composite faces; 1.8.5 Designs for calibration of item parameters in item response theory models; 1.9 Summary; 2 Designs for simple linear regression; 2.1 Design problem for a linear model; 2.1.1 The design; 2.1.2 The linear regression model; 2.1.3 Estimation of parameters and efficiency; 2.2 Designs for radiation-dosage example 2.3 Relative efficiency and sample size2.4 Simultaneous inference; 2.5 Optimality criteria; 2.5.1 D-optimality criterion; 2.5.2 A-optimality criterion; 2.5.3 G-optimality criterion; 2.5.4 E-optimality criterion; 2.5.5 Number of distinct design points; 2.6 Relative efficiency; 2.7 Matrix formulation of designs for linear regression; 2.8 Summary; 3 Designs for multiple linear regression analysis; 3.1 Design problem for multiple linear regression; 3.1.1 The design; 3.1.2 The multiple linear regression model; 3.1.3 Estimation of parameters and ef.ciency; 3.2 Designs for vocabulary-growth study 3.3 Relative efficiency and sample size3.4 Simultaneous inference; 3.5 Optimality criteria for a subset of parameters; 3.6 Relative efficiency; 3.7 Designs for polynomial regression model; 3.7.1 Exact D-optimal designs for a quadratic regression model; 3.7.2 Scale dependency of A- and E-optimality criteria; 3.8 The Poggendorff and Ponzo illusion study; 3.9 Uncertainty about best .tting regression models; 3.10 Matrix notation of designs for multiple regression models; 3.10.1 Design for regression models with two independent variables 3.10.2 Design for regression models with two non-additive independent variables3.11 Summary; 4 Designs for analysis of variance models; 4.1 A typical design problem for an analysis of variance model; 4.1.1 The design; 4.1.2 The analysis of variance model; 4.1.3 Formulation of an ANOVA model as a regression model; 4.2 Estimation of parameters and efficiency; 4.2.1 Measures of uncertainty; 4.3 Simultaneous inference and optimality criteria; 4.4 Designs for groups under stress study; 4.4.1 A priori planned unequal sample sizes; 4.4.2 Not planned unequal sample sizes 4.5 Specific hypotheses and contrasts |
| Record Nr. | UNINA-9910139920003321 |
Berger Martijn P. F
|
||
| Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to optimal designs for social and biomedical research [[electronic resource] /] / Martijn P.F. Berger, Weng Kee Wong
| An introduction to optimal designs for social and biomedical research [[electronic resource] /] / Martijn P.F. Berger, Weng Kee Wong |
| Autore | Berger Martijn P. F |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (348 p.) |
| Disciplina | 300.72 |
| Altri autori (Persone) | WongWeng Kee |
| Collana | Statistics in Practice |
| Soggetto topico |
Social sciences - Research
Biology - Research Optimal designs (Statistics) |
| ISBN |
1-282-18889-5
9786612188893 0-470-74691-2 0-470-74692-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
An Introduction to Optimal Designs for Social and Biomedical Research; Contents; Preface; Acknowledgements; 1 Introduction to designs; 1.1 Introduction; 1.2 Stages of the research process; 1.2.1 Choice of a 'good' design; 1.3 Research design; 1.3.1 Choice of independent variables and levels; 1.3.2 Units of analysis; 1.3.3 Variables; 1.3.4 Replication; 1.4 Types of research designs; 1.5 Requirements for a 'good' design; 1.5.1 Statistical conclusion validity; 1.5.2 Internal validity; 1.5.3 Control of (unwanted) variation; 1.6 Ethical aspects of design choice
1.7 Exact versus approximate designs1.8 Examples; 1.8.1 Radiation dosage example; 1.8.2 Designs for the Poggendorff and Ponzo illusion experiments; 1.8.3 Uncertainty about best .tting regression models; 1.8.4 Designs for a priori contrasts among composite faces; 1.8.5 Designs for calibration of item parameters in item response theory models; 1.9 Summary; 2 Designs for simple linear regression; 2.1 Design problem for a linear model; 2.1.1 The design; 2.1.2 The linear regression model; 2.1.3 Estimation of parameters and efficiency; 2.2 Designs for radiation-dosage example 2.3 Relative efficiency and sample size2.4 Simultaneous inference; 2.5 Optimality criteria; 2.5.1 D-optimality criterion; 2.5.2 A-optimality criterion; 2.5.3 G-optimality criterion; 2.5.4 E-optimality criterion; 2.5.5 Number of distinct design points; 2.6 Relative efficiency; 2.7 Matrix formulation of designs for linear regression; 2.8 Summary; 3 Designs for multiple linear regression analysis; 3.1 Design problem for multiple linear regression; 3.1.1 The design; 3.1.2 The multiple linear regression model; 3.1.3 Estimation of parameters and ef.ciency; 3.2 Designs for vocabulary-growth study 3.3 Relative efficiency and sample size3.4 Simultaneous inference; 3.5 Optimality criteria for a subset of parameters; 3.6 Relative efficiency; 3.7 Designs for polynomial regression model; 3.7.1 Exact D-optimal designs for a quadratic regression model; 3.7.2 Scale dependency of A- and E-optimality criteria; 3.8 The Poggendorff and Ponzo illusion study; 3.9 Uncertainty about best .tting regression models; 3.10 Matrix notation of designs for multiple regression models; 3.10.1 Design for regression models with two independent variables 3.10.2 Design for regression models with two non-additive independent variables3.11 Summary; 4 Designs for analysis of variance models; 4.1 A typical design problem for an analysis of variance model; 4.1.1 The design; 4.1.2 The analysis of variance model; 4.1.3 Formulation of an ANOVA model as a regression model; 4.2 Estimation of parameters and efficiency; 4.2.1 Measures of uncertainty; 4.3 Simultaneous inference and optimality criteria; 4.4 Designs for groups under stress study; 4.4.1 A priori planned unequal sample sizes; 4.4.2 Not planned unequal sample sizes 4.5 Specific hypotheses and contrasts |
| Record Nr. | UNINA-9910830940503321 |
|
Berger Martijn P. F
|
||
| Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to optimal designs for social and biomedical research / / Martijn P.F. Berger, Weng Kee Wong
| An introduction to optimal designs for social and biomedical research / / Martijn P.F. Berger, Weng Kee Wong |
| Autore | Berger Martijn P. F |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (348 p.) |
| Disciplina | 300.72 |
| Altri autori (Persone) | WongWeng Kee |
| Collana | Statistics in Practice |
| Soggetto topico |
Social sciences - Research
Biology - Research Optimal designs (Statistics) |
| ISBN |
1-282-18889-5
9786612188893 0-470-74691-2 0-470-74692-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
An Introduction to Optimal Designs for Social and Biomedical Research; Contents; Preface; Acknowledgements; 1 Introduction to designs; 1.1 Introduction; 1.2 Stages of the research process; 1.2.1 Choice of a 'good' design; 1.3 Research design; 1.3.1 Choice of independent variables and levels; 1.3.2 Units of analysis; 1.3.3 Variables; 1.3.4 Replication; 1.4 Types of research designs; 1.5 Requirements for a 'good' design; 1.5.1 Statistical conclusion validity; 1.5.2 Internal validity; 1.5.3 Control of (unwanted) variation; 1.6 Ethical aspects of design choice
1.7 Exact versus approximate designs1.8 Examples; 1.8.1 Radiation dosage example; 1.8.2 Designs for the Poggendorff and Ponzo illusion experiments; 1.8.3 Uncertainty about best .tting regression models; 1.8.4 Designs for a priori contrasts among composite faces; 1.8.5 Designs for calibration of item parameters in item response theory models; 1.9 Summary; 2 Designs for simple linear regression; 2.1 Design problem for a linear model; 2.1.1 The design; 2.1.2 The linear regression model; 2.1.3 Estimation of parameters and efficiency; 2.2 Designs for radiation-dosage example 2.3 Relative efficiency and sample size2.4 Simultaneous inference; 2.5 Optimality criteria; 2.5.1 D-optimality criterion; 2.5.2 A-optimality criterion; 2.5.3 G-optimality criterion; 2.5.4 E-optimality criterion; 2.5.5 Number of distinct design points; 2.6 Relative efficiency; 2.7 Matrix formulation of designs for linear regression; 2.8 Summary; 3 Designs for multiple linear regression analysis; 3.1 Design problem for multiple linear regression; 3.1.1 The design; 3.1.2 The multiple linear regression model; 3.1.3 Estimation of parameters and ef.ciency; 3.2 Designs for vocabulary-growth study 3.3 Relative efficiency and sample size3.4 Simultaneous inference; 3.5 Optimality criteria for a subset of parameters; 3.6 Relative efficiency; 3.7 Designs for polynomial regression model; 3.7.1 Exact D-optimal designs for a quadratic regression model; 3.7.2 Scale dependency of A- and E-optimality criteria; 3.8 The Poggendorff and Ponzo illusion study; 3.9 Uncertainty about best .tting regression models; 3.10 Matrix notation of designs for multiple regression models; 3.10.1 Design for regression models with two independent variables 3.10.2 Design for regression models with two non-additive independent variables3.11 Summary; 4 Designs for analysis of variance models; 4.1 A typical design problem for an analysis of variance model; 4.1.1 The design; 4.1.2 The analysis of variance model; 4.1.3 Formulation of an ANOVA model as a regression model; 4.2 Estimation of parameters and efficiency; 4.2.1 Measures of uncertainty; 4.3 Simultaneous inference and optimality criteria; 4.4 Designs for groups under stress study; 4.4.1 A priori planned unequal sample sizes; 4.4.2 Not planned unequal sample sizes 4.5 Specific hypotheses and contrasts |
| Record Nr. | UNINA-9910877667703321 |
|
Berger Martijn P. F
|
||
| Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Developments and Applications in Experimental Design
| New Developments and Applications in Experimental Design |
| Autore | Flournoy Nancy |
| Pubbl/distr/stampa | [Place of publication not identified], : Institute of Mathematical Statistics, 1998 |
| Disciplina | 519.5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910482888003321 |
|
Flournoy Nancy
|
||
| [Place of publication not identified], : Institute of Mathematical Statistics, 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
New Developments and Applications in Experimental Design
| New Developments and Applications in Experimental Design |
| Autore | Flournoy Nancy |
| Pubbl/distr/stampa | [Place of publication not identified], : Institute of Mathematical Statistics, 1998 |
| Disciplina | 519.5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996210822503316 |
|
Flournoy Nancy
|
||
| [Place of publication not identified], : Institute of Mathematical Statistics, 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||