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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
Alternative methods of regression [[electronic resource] /] / David Birkes, Yadolah Dodge
Alternative methods of regression [[electronic resource] /] / David Birkes, Yadolah Dodge
Autore Birkes David
Pubbl/distr/stampa New York, : John Wiley, 1993
Descrizione fisica 1 online resource (248 p.)
Disciplina 519.5/36
519.536
Altri autori (Persone) DodgeYadolah <1944->
Collana Wiley Series in Probability and Statistics
Soggetto topico Regression analysis
Multivariate analysis
Soggetto genere / forma Electronic books.
ISBN 1-283-29481-8
9786613294814
1-118-15023-6
1-118-15024-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Alternative Methods of Regression; Contents; Preface; Acknowledgments; 1. Linear Regression Analysis; 1.1 Introduction; 1.2 Example; 1.3 The Linear Regression Model; 1.4 Estimating the Regression Coefficients; 1.5 Testing the Significance of the Relationship; 1.6 The Need for Alternative Methods; 1.7 The Origin of the Word ""Regression""; Notes; References; 2. Constructing and Checking the Model; 2.1 Introduction; 2.2 Checking the Model; 2.3 Modifying the Model; 2.4 Examples; Notes; References; 3. Least-Squares Regression; 3.1 Introduction; 3.2 An Example of Simple Regression
3.3 Estimating the Regression Line3.4 Testing β = 0; 3.5 Checking Normality; 3.6 An Example of Multiple Regression; 3.7 Estimating the Regression Coefficients; 3.8 Testing the Regression Coefficients; 3.9 Testing βq + 1 = · · · = βp = 0; 3.10 Testing β3 = 0; 3.11 The Coefficient of Determination; 3.12 Computation; Notes; References; 4. Least-Absolute-Deviations Regression; 4.1 Introduction; 4.2 Estimating the Regression Line; 4.3 Nonuniqueness and Degeneracy; 4.4 Testing β = 0; 4.5 An Example of Multiple Regression; 4.6 Estimating the Regression Coefficients
4.7 Testing βq + 1 = · · · = βp = 04.8 Computation; Notes; References; 5. M-Regression; 5.1 Introduction; 5.2 An Example of Simple Regression; 5.3 Estimating the Regression Line; 5.4 Testing β = 0; 5.5 An Example of Multiple Regression; 5.6 Estimating the Regression Coefficients; 5.7 Testing βq + 1 = · · · = βp = 0; 5.8 Computation; Notes; References; 6. Nonparametric Regression; 6.1 Introduction; 6.2 An Example of Simple Regression; 6.3 Estimating the Regression Line; 6.4 Testing β = 0; 6.5 An Example of Multiple Regression; 6.6 Estimating the Regression Coefficients
6.7 Testing βq + 1 = · · · = βp = 06.8 Computation; Notes; References; 7. Bayesian Regression; 7.1 Introduction; 7.2 The Bayesian Approach; 7.3 An Example of Simple Regression; 7.4 Estimating the Regression Line; 7.5 Testing β = 0; 7.6 An Example of Multiple Regression; 7.7 Estimating the Regression Coefficients; 7.8 Testing βq + 1 = · · · = βp = 0; 7.9 Computation; Notes; References; 8. Ridge Regression; 8.1 Introduction; 8.2 An Example of Simple Regression; 8.3 Estimating the Regression Line; 8.4 An Example of Multiple Regression; 8.5 Standardization
8.6 Estimating the Regression Coefficients8.7 Collinearity; Notes; References; 9. Comparisons; 9.1 Introduction; 9.2 Comparison of Properties; 9.3 Comparisons on Three Data Sets; Notes; References; 10. Other Methods; 10.1 Introduction; 10.2 Other Methods of Linear Regression; 10.3 More General Methods of Regression; References; Appendix; Student's t-Distribution; F-Distribution; Chi-squared Distribution; Index
Record Nr. UNINA-9910139573103321
Birkes David  
New York, : John Wiley, 1993
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Alternative methods of regression [[electronic resource] /] / David Birkes, Yadolah Dodge
Alternative methods of regression [[electronic resource] /] / David Birkes, Yadolah Dodge
Autore Birkes David
Pubbl/distr/stampa New York, : John Wiley, 1993
Descrizione fisica 1 online resource (248 p.)
Disciplina 519.5/36
519.536
Altri autori (Persone) DodgeYadolah <1944->
Collana Wiley Series in Probability and Statistics
Soggetto topico Regression analysis
Multivariate analysis
ISBN 1-283-29481-8
9786613294814
1-118-15023-6
1-118-15024-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Alternative Methods of Regression; Contents; Preface; Acknowledgments; 1. Linear Regression Analysis; 1.1 Introduction; 1.2 Example; 1.3 The Linear Regression Model; 1.4 Estimating the Regression Coefficients; 1.5 Testing the Significance of the Relationship; 1.6 The Need for Alternative Methods; 1.7 The Origin of the Word ""Regression""; Notes; References; 2. Constructing and Checking the Model; 2.1 Introduction; 2.2 Checking the Model; 2.3 Modifying the Model; 2.4 Examples; Notes; References; 3. Least-Squares Regression; 3.1 Introduction; 3.2 An Example of Simple Regression
3.3 Estimating the Regression Line3.4 Testing β = 0; 3.5 Checking Normality; 3.6 An Example of Multiple Regression; 3.7 Estimating the Regression Coefficients; 3.8 Testing the Regression Coefficients; 3.9 Testing βq + 1 = · · · = βp = 0; 3.10 Testing β3 = 0; 3.11 The Coefficient of Determination; 3.12 Computation; Notes; References; 4. Least-Absolute-Deviations Regression; 4.1 Introduction; 4.2 Estimating the Regression Line; 4.3 Nonuniqueness and Degeneracy; 4.4 Testing β = 0; 4.5 An Example of Multiple Regression; 4.6 Estimating the Regression Coefficients
4.7 Testing βq + 1 = · · · = βp = 04.8 Computation; Notes; References; 5. M-Regression; 5.1 Introduction; 5.2 An Example of Simple Regression; 5.3 Estimating the Regression Line; 5.4 Testing β = 0; 5.5 An Example of Multiple Regression; 5.6 Estimating the Regression Coefficients; 5.7 Testing βq + 1 = · · · = βp = 0; 5.8 Computation; Notes; References; 6. Nonparametric Regression; 6.1 Introduction; 6.2 An Example of Simple Regression; 6.3 Estimating the Regression Line; 6.4 Testing β = 0; 6.5 An Example of Multiple Regression; 6.6 Estimating the Regression Coefficients
6.7 Testing βq + 1 = · · · = βp = 06.8 Computation; Notes; References; 7. Bayesian Regression; 7.1 Introduction; 7.2 The Bayesian Approach; 7.3 An Example of Simple Regression; 7.4 Estimating the Regression Line; 7.5 Testing β = 0; 7.6 An Example of Multiple Regression; 7.7 Estimating the Regression Coefficients; 7.8 Testing βq + 1 = · · · = βp = 0; 7.9 Computation; Notes; References; 8. Ridge Regression; 8.1 Introduction; 8.2 An Example of Simple Regression; 8.3 Estimating the Regression Line; 8.4 An Example of Multiple Regression; 8.5 Standardization
8.6 Estimating the Regression Coefficients8.7 Collinearity; Notes; References; 9. Comparisons; 9.1 Introduction; 9.2 Comparison of Properties; 9.3 Comparisons on Three Data Sets; Notes; References; 10. Other Methods; 10.1 Introduction; 10.2 Other Methods of Linear Regression; 10.3 More General Methods of Regression; References; Appendix; Student's t-Distribution; F-Distribution; Chi-squared Distribution; Index
Record Nr. UNISA-996218078503316
Birkes David  
New York, : John Wiley, 1993
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Alternative methods of regression [[electronic resource] /] / David Birkes, Yadolah Dodge
Alternative methods of regression [[electronic resource] /] / David Birkes, Yadolah Dodge
Autore Birkes David
Pubbl/distr/stampa New York, : John Wiley, 1993
Descrizione fisica 1 online resource (248 p.)
Disciplina 519.5/36
519.536
Altri autori (Persone) DodgeYadolah <1944->
Collana Wiley Series in Probability and Statistics
Soggetto topico Regression analysis
Multivariate analysis
ISBN 1-283-29481-8
9786613294814
1-118-15023-6
1-118-15024-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Alternative Methods of Regression; Contents; Preface; Acknowledgments; 1. Linear Regression Analysis; 1.1 Introduction; 1.2 Example; 1.3 The Linear Regression Model; 1.4 Estimating the Regression Coefficients; 1.5 Testing the Significance of the Relationship; 1.6 The Need for Alternative Methods; 1.7 The Origin of the Word ""Regression""; Notes; References; 2. Constructing and Checking the Model; 2.1 Introduction; 2.2 Checking the Model; 2.3 Modifying the Model; 2.4 Examples; Notes; References; 3. Least-Squares Regression; 3.1 Introduction; 3.2 An Example of Simple Regression
3.3 Estimating the Regression Line3.4 Testing β = 0; 3.5 Checking Normality; 3.6 An Example of Multiple Regression; 3.7 Estimating the Regression Coefficients; 3.8 Testing the Regression Coefficients; 3.9 Testing βq + 1 = · · · = βp = 0; 3.10 Testing β3 = 0; 3.11 The Coefficient of Determination; 3.12 Computation; Notes; References; 4. Least-Absolute-Deviations Regression; 4.1 Introduction; 4.2 Estimating the Regression Line; 4.3 Nonuniqueness and Degeneracy; 4.4 Testing β = 0; 4.5 An Example of Multiple Regression; 4.6 Estimating the Regression Coefficients
4.7 Testing βq + 1 = · · · = βp = 04.8 Computation; Notes; References; 5. M-Regression; 5.1 Introduction; 5.2 An Example of Simple Regression; 5.3 Estimating the Regression Line; 5.4 Testing β = 0; 5.5 An Example of Multiple Regression; 5.6 Estimating the Regression Coefficients; 5.7 Testing βq + 1 = · · · = βp = 0; 5.8 Computation; Notes; References; 6. Nonparametric Regression; 6.1 Introduction; 6.2 An Example of Simple Regression; 6.3 Estimating the Regression Line; 6.4 Testing β = 0; 6.5 An Example of Multiple Regression; 6.6 Estimating the Regression Coefficients
6.7 Testing βq + 1 = · · · = βp = 06.8 Computation; Notes; References; 7. Bayesian Regression; 7.1 Introduction; 7.2 The Bayesian Approach; 7.3 An Example of Simple Regression; 7.4 Estimating the Regression Line; 7.5 Testing β = 0; 7.6 An Example of Multiple Regression; 7.7 Estimating the Regression Coefficients; 7.8 Testing βq + 1 = · · · = βp = 0; 7.9 Computation; Notes; References; 8. Ridge Regression; 8.1 Introduction; 8.2 An Example of Simple Regression; 8.3 Estimating the Regression Line; 8.4 An Example of Multiple Regression; 8.5 Standardization
8.6 Estimating the Regression Coefficients8.7 Collinearity; Notes; References; 9. Comparisons; 9.1 Introduction; 9.2 Comparison of Properties; 9.3 Comparisons on Three Data Sets; Notes; References; 10. Other Methods; 10.1 Introduction; 10.2 Other Methods of Linear Regression; 10.3 More General Methods of Regression; References; Appendix; Student's t-Distribution; F-Distribution; Chi-squared Distribution; Index
Record Nr. UNINA-9910831025803321
Birkes David  
New York, : John Wiley, 1993
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Alternative methods of regression / / David Birkes, Yadolah Dodge
Alternative methods of regression / / David Birkes, Yadolah Dodge
Autore Birkes David
Pubbl/distr/stampa New York, : John Wiley, 1993
Descrizione fisica 1 online resource (248 p.)
Disciplina 519.5/36
519.536
Altri autori (Persone) DodgeYadolah <1944->
Collana Wiley Series in Probability and Statistics
Soggetto topico Regression analysis
Multivariate analysis
ISBN 1-283-29481-8
9786613294814
1-118-15023-6
1-118-15024-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Alternative Methods of Regression; Contents; Preface; Acknowledgments; 1. Linear Regression Analysis; 1.1 Introduction; 1.2 Example; 1.3 The Linear Regression Model; 1.4 Estimating the Regression Coefficients; 1.5 Testing the Significance of the Relationship; 1.6 The Need for Alternative Methods; 1.7 The Origin of the Word ""Regression""; Notes; References; 2. Constructing and Checking the Model; 2.1 Introduction; 2.2 Checking the Model; 2.3 Modifying the Model; 2.4 Examples; Notes; References; 3. Least-Squares Regression; 3.1 Introduction; 3.2 An Example of Simple Regression
3.3 Estimating the Regression Line3.4 Testing β = 0; 3.5 Checking Normality; 3.6 An Example of Multiple Regression; 3.7 Estimating the Regression Coefficients; 3.8 Testing the Regression Coefficients; 3.9 Testing βq + 1 = · · · = βp = 0; 3.10 Testing β3 = 0; 3.11 The Coefficient of Determination; 3.12 Computation; Notes; References; 4. Least-Absolute-Deviations Regression; 4.1 Introduction; 4.2 Estimating the Regression Line; 4.3 Nonuniqueness and Degeneracy; 4.4 Testing β = 0; 4.5 An Example of Multiple Regression; 4.6 Estimating the Regression Coefficients
4.7 Testing βq + 1 = · · · = βp = 04.8 Computation; Notes; References; 5. M-Regression; 5.1 Introduction; 5.2 An Example of Simple Regression; 5.3 Estimating the Regression Line; 5.4 Testing β = 0; 5.5 An Example of Multiple Regression; 5.6 Estimating the Regression Coefficients; 5.7 Testing βq + 1 = · · · = βp = 0; 5.8 Computation; Notes; References; 6. Nonparametric Regression; 6.1 Introduction; 6.2 An Example of Simple Regression; 6.3 Estimating the Regression Line; 6.4 Testing β = 0; 6.5 An Example of Multiple Regression; 6.6 Estimating the Regression Coefficients
6.7 Testing βq + 1 = · · · = βp = 06.8 Computation; Notes; References; 7. Bayesian Regression; 7.1 Introduction; 7.2 The Bayesian Approach; 7.3 An Example of Simple Regression; 7.4 Estimating the Regression Line; 7.5 Testing β = 0; 7.6 An Example of Multiple Regression; 7.7 Estimating the Regression Coefficients; 7.8 Testing βq + 1 = · · · = βp = 0; 7.9 Computation; Notes; References; 8. Ridge Regression; 8.1 Introduction; 8.2 An Example of Simple Regression; 8.3 Estimating the Regression Line; 8.4 An Example of Multiple Regression; 8.5 Standardization
8.6 Estimating the Regression Coefficients8.7 Collinearity; Notes; References; 9. Comparisons; 9.1 Introduction; 9.2 Comparison of Properties; 9.3 Comparisons on Three Data Sets; Notes; References; 10. Other Methods; 10.1 Introduction; 10.2 Other Methods of Linear Regression; 10.3 More General Methods of Regression; References; Appendix; Student's t-Distribution; F-Distribution; Chi-squared Distribution; Index
Record Nr. UNINA-9910877756703321
Birkes David  
New York, : John Wiley, 1993
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied linear regression / / Sanford Weisberg
Applied linear regression / / Sanford Weisberg
Autore Weisberg Sanford <1947->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (370 pages) : illustrations
Disciplina 519.5/36
Collana Wiley Series in Probability and Statistics
Soggetto topico Regression analysis
ISBN 1118594851
9781118594858
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910795955703321
Weisberg Sanford <1947->  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied linear regression / / Sanford Weisberg
Applied linear regression / / Sanford Weisberg
Autore Weisberg Sanford <1947->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (370 pages) : illustrations
Disciplina 519.5/36
Collana Wiley Series in Probability and Statistics
Soggetto topico Regression analysis
ISBN 1118594851
9781118594858
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910814698803321
Weisberg Sanford <1947->  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied logistic regression [[electronic resource] ] : David W. Hosmer, Stanley Lemeshow, Rodney X. Sturdivant
Applied logistic regression [[electronic resource] ] : David W. Hosmer, Stanley Lemeshow, Rodney X. Sturdivant
Autore Hosmer David W
Edizione [3rd ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2013
Descrizione fisica 1 online resource (528 p.)
Disciplina 519.5/36
Altri autori (Persone) LemeshowStanley
SturdivantRodney X
Collana Wiley series in probability and statistics
Soggetto topico Regression analysis
Anàlisi de regressió
Anàlisi multivariable
Estadística
Soggetto genere / forma Llibres electrònics
ISBN 1-118-54838-8
1-118-54835-3
1-299-40240-2
1-118-54839-6
Classificazione MAT029030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Logistic Regression; Contents; Preface to the Third Edition; 1 Introduction to the Logistic Regression Model; 1.1 Introduction; 1.2 Fitting the Logistic Regression Model; 1.3 Testing for the Significance of the Coefficients; 1.4 Confidence Interval Estimation; 1.5 Other Estimation Methods; 1.6 Data Sets Used in Examples and Exercises; 1.6.1 The ICU Study; 1.6.2 The Low Birth Weight Study; 1.6.3 The Global Longitudinal Study of Osteoporosis in Women; 1.6.4 The Adolescent Placement Study; 1.6.5 The Burn Injury Study; 1.6.6 The Myopia Study; 1.6.7 The NHANES Study
1.6.8 The Polypharmacy StudyExercises; 2 The Multiple Logistic Regression Model; 2.1 Introduction; 2.2 The Multiple Logistic Regression Model; 2.3 Fitting the Multiple Logistic Regression Model; 2.4 Testing for the Significance of the Model; 2.5 Confidence Interval Estimation; 2.6 Other Estimation Methods; Exercises; 3 Interpretation of the Fitted Logistic Regression Model; 3.1 Introduction; 3.2 Dichotomous Independent Variable; 3.3 Polychotomous Independent Variable; 3.4 Continuous Independent Variable; 3.5 Multivariable Models; 3.6 Presentation and Interpretation of the Fitted Values
3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 x 2 TablesExercises; 4 Model-Building Strategies and Methods for Logistic Regression; 4.1 Introduction; 4.2 Purposeful Selection of Covariates; 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit; 4.2.2 Examples of Purposeful Selection; 4.3 Other Methods for Selecting Covariates; 4.3.1 Stepwise Selection of Covariates; 4.3.2 Best Subsets Logistic Regression; 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials; 4.4 Numerical Problems; Exercises
5 Assessing the Fit of the Model5.1 Introduction; 5.2 Summary Measures of Goodness of Fit; 5.2.1 Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares; 5.2.2 The Hosmer-Lemeshow Tests; 5.2.3 Classification Tables; 5.2.4 Area Under the Receiver Operating Characteristic Curve; 5.2.5 Other Summary Measures; 5.3 Logistic Regression Diagnostics; 5.4 Assessment of Fit via External Validation; 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model; Exercises; 6 Application of Logistic Regression with Different Sampling Models; 6.1 Introduction
6.2 Cohort Studies6.3 Case-Control Studies; 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys; Exercises; 7 Logistic Regression for Matched Case-Control Studies; 7.1 Introduction; 7.2 Methods For Assessment of Fit in a 1-M Matched Study; 7.3 An Example Using the Logistic Regression Model in a 1-1 Matched Study; 7.4 An Example Using the Logistic Regression Model in a 1-M Matched Study; Exercises; 8 Logistic Regression Models for Multinomial and Ordinal Outcomes; 8.1 The Multinomial Logistic Regression Model
8.1.1 Introduction to the Model and Estimation of Model Parameters
Record Nr. UNINA-9910139038403321
Hosmer David W  
Hoboken, N.J., : Wiley, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Applied quantitative analysis in education and the social sciences / / edited by Yaacov Petscher, Christopher Schatschneider, Donald L. Compton
Applied quantitative analysis in education and the social sciences / / edited by Yaacov Petscher, Christopher Schatschneider, Donald L. Compton
Pubbl/distr/stampa New York : , : Routledge, , 2013
Descrizione fisica 1 online resource (389 p.)
Disciplina 519.5/36
Altri autori (Persone) ComptonDonald L. <1960->
PetscherYaacov M
SchatschneiderChristopher
Soggetto topico Regression analysis
Mathematical statistics
Soggetto genere / forma Electronic books.
ISBN 0-203-10855-8
1-299-27868-X
1-136-26633-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Extending conditional means modeling: an introduction to quantile regression / Yaacov Petscher, Jessica A.R. Logan, and Chengfu Zhou -- Using dominance analysis to estimate predictor importance in multiple regression / Razia Azen -- I am ROC curves (and so can you)! / Christopher Schatschneider -- Multilevel modeling: practical examples to illustrate a special case of SEM / Lee Branum-Martin -- Linear and quadratic growth models for continuous and dichotomous outcomes / Ann A. O'Connell, Jessica A. R. Logan, Jill Pentimonti, and D. Betsy McCoach -- Exploratory and confirmatory factor analysis / Rex Kline -- Factor analysis with categorical indicators: demonstration of item response theory / R.J. de Ayala -- Introduction to structural equation modeling / Richard Lomax -- Latent growth curve modeling using structural equation modeling / Ryan Bowles and Janelle J. Montroy -- Latent class/profile analysis / Karen Samuelsen and Katherine Raczynski -- n-level structural equation modeling / Paras Mehta.
Record Nr. UNINA-9910465626603321
New York : , : Routledge, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui