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Adaptive tests of significance using permutations of residuals with R and SAS [[electronic resource] /] / Thomas W. O'Gorman
Adaptive tests of significance using permutations of residuals with R and SAS [[electronic resource] /] / Thomas W. O'Gorman
Autore O'Gorman Thomas W
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (365 p.)
Disciplina 519.5/36
Soggetto topico Regression analysis
Computer adaptive testing
R (Computer program language)
ISBN 1-280-58894-2
1-118-21825-6
9786613618771
1-118-21822-1
Classificazione MAT029030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Tests of Significance Using Permutations of Residuals with R and SAS®; CONTENTS; Preface; 1 Introduction; 1.1 Why Use Adaptive Tests?; 1.2 A Brief History of Adaptive Tests; 1.2.1 Early Tests and Estimators; 1.2.2 Rank Tests; 1.2.3 The Weighted Least Squares Approach; 1.2.4 Recent Rank-Based Tests; 1.3 The Adaptive Test of Hogg, Fisher, and Randles; 1.3.1 Level of Significance of the HFR Test; 1.3.2 Comparison of Power of the HFR Test to the t Test; 1.4 Limitations of Rank-Based Tests; 1.5 The Adaptive Weighted Least Squares Approach; 1.5.1 Level of Significance
1.5.2 Comparison of Power of the Adaptive WLS Test to the t Test and the HFR Test1.6 Development of the Adaptive WLS Test; 2 Smoothing Methods and Normalizing Transformations; 2.1 Traditional Estimators of the Median and the Interquartile Range; 2.2 Percentile Estimators that Use the Smooth Cumulative Distribution Function; 2.2.1 Smoothing the Cumulative Distribution Function; 2.2.2 Using the Smoothed c.d.f. to Compute Percentiles; 2.2.3 R Code for Smoothing the c.d.f.; 2.2.4 R Code for Finding Percentiles; 2.3 Estimating the Bandwidth
2.3.1 An Estimator of Variability Based on Traditional Percentiles2.3.2 R Code for Finding the Bandwidth; 2.3.3 An Estimator of Variability Based on Percentiles from the Smoothed Distribution Function; 2.4 Normalizing Transformations; 2.4.1 Traditional Normalizing Methods; 2.4.2 Normalizing Data by Weighting; 2.5 The Weighting Algorithm; 2.5.1 An Example of the Weighing Procedure; 2.5.2 R Code for Weighting the Observations; 2.6 Computing the Bandwidth; 2.6.1 Error Distributions; 2.6.2 Measuring Errors in Adaptive Weighting; 2.6.3 Simulation Studies; 2.7 Examples of Transformed Data
Exercises3 A Two-Sample Adaptive Test; 3.1 A Two-Sample Model; 3.2 Computing the Adaptive Weights; 3.2.1 R Code for Computing the Weights; 3.3 The Test Statistics for Adaptive Tests; 3.3.1 R Code to Compute the Test Statistic; 3.4 Permutation Methods for Two-Sample Tests; 3.4.1 Permutation of Observations; 3.4.2 Permutation of Residuals; 3.4.3 R Code for Permutations; 3.5 An Example of a Two-Sample Test; 3.6 R Code for the Two-Sample Test; 3.6.1 R Code for Computing the Test Statistics; 3.6.2 R Code to Compute the Traditional F Test Statistic and p-Value
3.6.3 An R Function that Computes the p-Value for the Adaptive Test3.6.4 R Code to Perform the Adaptive Test; 3.7 Level of Significance of the Adaptive Test; 3.8 Power of the Adaptive Test; 3.9 Sample Size Estimation; 3.10 A SAS Macro for the Adaptive Test; 3.11 Modifications for One-Tailed Tests; 3.12 Justification of the Weighting Method; 3.13 Comments on the Adaptive Two-sample Test; Exercises; 4 Permutation Tests with Linear Models; 4.1 Introduction; 4.2 Notation; 4.3 Permutations with Blocking; 4.4 Linear Models in Matrix Form; 4.5 Permutation Methods; 4.5.1 The Permute-Errors Method
4.5.2 The Permute-Residuals Method
Record Nr. UNINA-9910141323703321
O'Gorman Thomas W  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Adaptive tests of significance using permutations of residuals with R and SAS / / Thomas W. O'Gorman
Adaptive tests of significance using permutations of residuals with R and SAS / / Thomas W. O'Gorman
Autore O'Gorman Thomas W
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (365 p.)
Disciplina 519.5/36
Soggetto topico Regression analysis
Computer adaptive testing
R (Computer program language)
ISBN 9786613618771
9781280588945
1280588942
9781118218259
1118218256
9781118218228
1118218221
Classificazione MAT029030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Tests of Significance Using Permutations of Residuals with R and SAS®; CONTENTS; Preface; 1 Introduction; 1.1 Why Use Adaptive Tests?; 1.2 A Brief History of Adaptive Tests; 1.2.1 Early Tests and Estimators; 1.2.2 Rank Tests; 1.2.3 The Weighted Least Squares Approach; 1.2.4 Recent Rank-Based Tests; 1.3 The Adaptive Test of Hogg, Fisher, and Randles; 1.3.1 Level of Significance of the HFR Test; 1.3.2 Comparison of Power of the HFR Test to the t Test; 1.4 Limitations of Rank-Based Tests; 1.5 The Adaptive Weighted Least Squares Approach; 1.5.1 Level of Significance
1.5.2 Comparison of Power of the Adaptive WLS Test to the t Test and the HFR Test1.6 Development of the Adaptive WLS Test; 2 Smoothing Methods and Normalizing Transformations; 2.1 Traditional Estimators of the Median and the Interquartile Range; 2.2 Percentile Estimators that Use the Smooth Cumulative Distribution Function; 2.2.1 Smoothing the Cumulative Distribution Function; 2.2.2 Using the Smoothed c.d.f. to Compute Percentiles; 2.2.3 R Code for Smoothing the c.d.f.; 2.2.4 R Code for Finding Percentiles; 2.3 Estimating the Bandwidth
2.3.1 An Estimator of Variability Based on Traditional Percentiles2.3.2 R Code for Finding the Bandwidth; 2.3.3 An Estimator of Variability Based on Percentiles from the Smoothed Distribution Function; 2.4 Normalizing Transformations; 2.4.1 Traditional Normalizing Methods; 2.4.2 Normalizing Data by Weighting; 2.5 The Weighting Algorithm; 2.5.1 An Example of the Weighing Procedure; 2.5.2 R Code for Weighting the Observations; 2.6 Computing the Bandwidth; 2.6.1 Error Distributions; 2.6.2 Measuring Errors in Adaptive Weighting; 2.6.3 Simulation Studies; 2.7 Examples of Transformed Data
Exercises3 A Two-Sample Adaptive Test; 3.1 A Two-Sample Model; 3.2 Computing the Adaptive Weights; 3.2.1 R Code for Computing the Weights; 3.3 The Test Statistics for Adaptive Tests; 3.3.1 R Code to Compute the Test Statistic; 3.4 Permutation Methods for Two-Sample Tests; 3.4.1 Permutation of Observations; 3.4.2 Permutation of Residuals; 3.4.3 R Code for Permutations; 3.5 An Example of a Two-Sample Test; 3.6 R Code for the Two-Sample Test; 3.6.1 R Code for Computing the Test Statistics; 3.6.2 R Code to Compute the Traditional F Test Statistic and p-Value
3.6.3 An R Function that Computes the p-Value for the Adaptive Test3.6.4 R Code to Perform the Adaptive Test; 3.7 Level of Significance of the Adaptive Test; 3.8 Power of the Adaptive Test; 3.9 Sample Size Estimation; 3.10 A SAS Macro for the Adaptive Test; 3.11 Modifications for One-Tailed Tests; 3.12 Justification of the Weighting Method; 3.13 Comments on the Adaptive Two-sample Test; Exercises; 4 Permutation Tests with Linear Models; 4.1 Introduction; 4.2 Notation; 4.3 Permutations with Blocking; 4.4 Linear Models in Matrix Form; 4.5 Permutation Methods; 4.5.1 The Permute-Errors Method
4.5.2 The Permute-Residuals Method
Record Nr. UNINA-9910811410703321
O'Gorman Thomas W  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Autore Stirrup Jen
Edizione [1st edition]
Pubbl/distr/stampa Birmingham : , : Packt, , 2017
Descrizione fisica 1 online resource (178 pages) : illustrations
Disciplina 658.4038011
Soggetto topico R (Computer program language)
Soggetto genere / forma Electronic books.
ISBN 1-5231-2523-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910467171903321
Stirrup Jen  
Birmingham : , : Packt, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Autore Stirrup Jen
Edizione [1st edition]
Pubbl/distr/stampa Birmingham : , : Packt, , 2017
Descrizione fisica 1 online resource (178 pages) : illustrations
Disciplina 658.4038011
Soggetto topico R (Computer program language)
ISBN 1-5231-2523-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910796534703321
Stirrup Jen  
Birmingham : , : Packt, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Autore Stirrup Jen
Edizione [1st edition]
Pubbl/distr/stampa Birmingham : , : Packt, , 2017
Descrizione fisica 1 online resource (178 pages) : illustrations
Disciplina 658.4038011
Soggetto topico R (Computer program language)
ISBN 1-5231-2523-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910827493103321
Stirrup Jen  
Birmingham : , : Packt, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Autore Rai Bharatendra
Edizione [1st edition]
Pubbl/distr/stampa Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Descrizione fisica 1 online resource (vii, 337 pages) : illustrations
Disciplina 519.502855133
Soggetto topico R (Computer program language)
ISBN 1-78953-498-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793816103321
Rai Bharatendra  
Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Autore Rai Bharatendra
Edizione [1st edition]
Pubbl/distr/stampa Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Descrizione fisica 1 online resource (vii, 337 pages) : illustrations
Disciplina 519.502855133
Soggetto topico R (Computer program language)
ISBN 1-78953-498-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910827078003321
Rai Bharatendra  
Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance / / by Thomas Mailund
Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance / / by Thomas Mailund
Autore Mailund Thomas
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017
Descrizione fisica 1 online resource (XV, 110 p. 10 illus.)
Disciplina 005.11
Soggetto topico Computer programming
Programming languages (Electronic computers)
Mathematical statistics
R (Computer program language)
Programming Techniques
Programming Languages, Compilers, Interpreters
Probability and Statistics in Computer Science
ISBN 9781484229194
1484229193
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Classes and Generic Functions -- 2. Class Hierarchies -- 3. Implementation Reuse -- 4. Statistical Models -- 5. Operator Overloading -- 6. S4 Classes -- 7. R6 Classes -- 8. Conclusions.
Record Nr. UNINA-9910254567203321
Mailund Thomas  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced R : data programming and the cloud / / by Matt Wiley, Joshua F. Wiley
Advanced R : data programming and the cloud / / by Matt Wiley, Joshua F. Wiley
Autore Wiley Matt
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2016
Descrizione fisica 1 online resource (XIX, 279 p. 77 illus., 40 illus. in color.)
Disciplina 005.13
Soggetto topico Programming languages (Electronic computers)
Mathematical statistics
Statistics
Computer programming
R (Computer program language)
Programming Languages, Compilers, Interpreters
Probability and Statistics in Computer Science
Statistics and Computing/Statistics Programs
Programming Techniques
ISBN 9781484220771
1484220773
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1.Programming Basics -- 2.Programming Utilities -- 3.Loops, flow control, and *apply functions -- 4.Writing Functions -- 5.Writing Classes and Methods -- 6.Writing a Package -- 7.Data Management using data.table -- 8.Data Munging With data.table -- 9.Other Tools for Data Management -- 10.Reading Big Data(bases) -- 11.Getting a Cloud -- 12.Ubuntu for Windows Users -- 13.Every Cloud has a Shiny lining -- 14.Shiny Dashboard Sampler -- 15.Dynamic Reports and the Cloud -- References.
Record Nr. UNINA-9910151576903321
Wiley Matt  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced R 4 Data Programming and the Cloud : Using PostgreSQL, AWS, and Shiny / / by Matt Wiley, Joshua F. Wiley
Advanced R 4 Data Programming and the Cloud : Using PostgreSQL, AWS, and Shiny / / by Matt Wiley, Joshua F. Wiley
Autore Wiley Matt
Edizione [2nd ed. 2020.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020
Descrizione fisica 1 online resource (XIII, 433 p. 65 illus., 9 illus. in color.)
Disciplina 005.133
Soggetto topico Programming languages (Electronic computers)
Mathematical statistics
Statistics
Computer programming
R (Computer program language)
Programming Languages, Compilers, Interpreters
Probability and Statistics in Computer Science
Statistics and Computing/Statistics Programs
Programming Techniques
ISBN 9781484259733
1484259734
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Programming Basics -- 2. Programming Utilities -- 3. Programming Automation -- 4. Writing Functions -- 5. Writing Classes and Methods -- 6. Writing Packages -- 7. Introduction to data.table -- 8. Advanced data.table -- 9. Other Data Management Packages -- 10. Reading Big Data -- 11. Getting Your Cloud -- 12. Cloud Ubuntu for Windows Users -- 13. Every Cloud has a Shiny lining -- 14. Shiny Dashboard Sampler -- 15. Dynamic Reports and the Cloud -- Bibliography.
Altri titoli varianti Advanced R four data programming and the cloud
Record Nr. UNINA-9910411930303321
Wiley Matt  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui