UID:
almafu_9959328322902883
Umfang:
1 online resource
Ausgabe:
2nd ed.
ISBN:
0470382805
,
9780470382806
,
9780470081860
,
0470081864
,
9781523118311
,
1523118318
Anmerkung:
1. Introduction. -- 1.1 Simple Linear Regression Model -- 1.2 Uses of Regression Models. -- 1.3 Graph the Data! -- 1.4 Estimation of ß〈sub〉0〈/sub〉 and ß〈sub〉1〈/sub〉. -- 1.5 Inferences from Regression Equations. -- 1.6 Regression Through the Origin. -- 1.7 Additional Examples. -- 1.8 Correlation. -- 1.9 Miscellaneous Uses of Regression. -- 1.10 Fixed Versus Random Regressors. -- 1.11 Missing Data. -- 1.12 Spurious Relationships. -- 1.13 Software. -- 1.14 Summary. -- Appendix. -- References. -- Exercises. -- 2. Diagnostics and Remedial Measures. -- 2.1 Assumptions. -- 2.2 Residual Plots. -- 2.3 Transformations. -- 2.4 Influential Observations. -- 2.5 Outliers. -- 2.6 Measurement Error. -- 2.7 Software. -- 2.8 Summary. -- Appendix. -- References. -- Exercises. -- 3. Regression with Matrix Algebra. -- 3.1 Introduction to Matrix Algebra. -- 3.2 Matrix Algebra Applied to Regression. -- 3.3 Summary. -- Appendix. -- References. -- Exercises. -- 4. Introduction to Multiple Linear Regression. -- 4.1 An Example of Multiple Linear Regression. -- 4.2 Centering And Scaling. -- 4.3 Interpreting Multiple Regression Coefficients. -- 4.4 Indicator Variables. -- 4.5 Separation or Not? -- 4.6 Alternatives to Multiple Regression. -- 4.7 Software. -- 4.8 Summary. -- References. -- Exercises. -- 5. Plots in Multiple Regression. -- 5.1 Beyond Standardized Residual Plots. -- 5.2 Some Examples. -- 5.3 Which Plot? -- 5.4 Recommendations. -- 5.5 Partial Regression Plots. -- 5.6 Other Plots For Detecting Influential Observations. -- 5.7 Recent Contributions to Plots in Multiple Regression. -- 5.8 Lurking Variables. -- 5.9 Explanation of Two Data Sets Relative to R〈sup〉2〈/sup〉. -- 5.10 Software. -- 5.11 Summary. -- References. -- Exercises. -- 6. Transformations in Multiple Regression. -- 6.1 Transforming Regressors. -- 6.2 Transforming Y. -- 6.3 Further Comments on the Normality Issue. -- 6.4 Box-Cox Transformation. -- 6.5 Box-Tidwell Revisited. -- 6.6 Combined Box-Cox and Box-Tidwell Approach. -- 6.7 Other Transformation Methods. -- 6.8 Transformation Diagnostics. -- 6.9 Software. -- 6.10 Summary. -- References. -- Exercises. -- 7. Selection of Regressors. -- 7.1 Forward Selection. -- 7.2 Backward Elimination. -- 7.3 Stepwise Regression. -- 7.4 All Possible Regressions. -- 7.5 Newer Methods. -- 7.6 Examples. -- 7.7 Variable Selection for Nonlinear Terms. -- 7.8 Must We Use a Subset? -- 7.9 Model Validation. -- 7.10 Software. -- 7.11 Summary. -- Appendix. -- References. -- Exercises. -- 8. Polynomial and Trigonometric Terms. -- 8.1 Polynomial Terms. -- 8.2 Polynomial-Trigonometric Regression. -- 8.3 Software. -- 8.4 Summary. -- References. -- Exercises. -- 9. Logistic Regression. -- 9.1 Introduction. -- 9.2 One Regressor. -- 9.3 A Simulated Example. -- 9.4 Detecting Complete Separation, Quasicomplete Separation and Near Separation. -- 9.5 Measuring the Worth of the Model. -- 9.6 Determining the Worth of the Individual Regressors. -- 9.7 Confidence Intervals. -- 9.8 Exact Prediction. -- 9.9 An Example With Real Data. -- 9.10 An Example of Multiple Logistic Regression. -- 9.11 Multicollinearity in Multiple Logistic Regression. -- 9.12 Osteogenic Sarcoma Data Set. -- 9.13 Missing Data. -- 9.14 Sample Size Determination. -- 9.15 Polytomous Logistic Regression. -- 9.16 Logistic Regression Variations. -- 9.17 Alternatives to Logistic Regression. -- 9.18 Software for Logistic Regression. -- 9.19 Summary. -- Appendix. -- References. -- Exercises. -- 10. Nonparametric Regression. -- 10.1 Relaxing Regression Assumptions. -- 10.2 Monotone Regression. -- 10.3 Smoothers. -- 10.4 Variable Selection. -- 10.5 Important Considerations in Smoothing. -- 10.6 Sliced Inverse Regression. -- 10.7 Projection Pursuit Regression. -- 10.8 Software. -- 10.9 Summary. -- Appendix. -- References. -- Exercises. -- 11. Robust Regression. -- 11.1 The Need for Robust Regression. -- 11.2 Types of Outliers. -- 11.3 Historical Development of Robust Regression. -- 11.4 Goals of Robust Regression. -- 11.5 Proposed High Breakdown Point Estimators. -- 11.6 Approximating HBP Estimator Solutions. -- 11.7 Other Methods for Detecting Multiple Outliers. -- 11.8 Bounded Influence Estimators. -- 11.9 Multistage Procedures. -- 11.10 Other Robust Regression Estimators. -- 11.11 Applications. -- 11.12 Software for Robust Regression. -- 11.13 Summary. -- References. -- Exercises. -- 12. Ridge Regression. -- 12.1 Introduction. -- 12.2 How Do We Determine 〈i〉K〈/i〉? -- 12.3 An Example. -- 12.4 Ridge Regression for Prediction. -- 12.5 Generalized Ridge Regression. -- 12.6 Inferences in Ridge Regression. -- 12.7 Some Practical Considerations. -- 12.8 Robust Ridge Regression? -- 12.9 Recent Developments in Ridge Regression. -- 12.10 Other Biased Estimators. -- 12.11 Software. -- 12.12 Summary. -- Appendix. -- References. -- Exercises. -- 13. Nonlinear Regression. -- 13.1 Introduction. -- 13.2 Linear Versus Nonlinear Regression. -- 13.3 A Simple Nonlinear Example. -- 13.4 Relative Offset Convergence Criterion. -- 13.5 Adequacy of the Estimation Approach. -- 13.6 Computational Considerations. -- 13.7 Determining Model Adequacy. -- 13.7.1 Lack-of-Fit Test. -- 13.8 Inferences. -- 13.9 An Application. -- 13.10 Rational Functions. -- 13.11 Robust Nonlinear Regression. -- 13.12 Applications. -- 13.13 Teaching Tools. -- 13.14 Recent Developments. -- 13.15 Software. -- 13.16 Summary. -- Appendix. -- References. -- Exercises. -- 14. Experimental Designs for Regression. -- 14.1 Objectives for Experimental Designs. -- 14.2 Equal Leverage Points. -- 14.3 Other Desirable Properties of Experimental Designs. -- 14.4 Model Misspecification. -- 14.5 Range of Regressors. -- 14.6 Algorithms for Design Construction. -- 14.7 Designs for Polynomial Regression. -- 14.8 Designs for Logistic Regression. -- 14.9 Designs for Nonlinear Regression. -- 14.10 Software. -- 14.11 Summary. -- References. -- Exercises. -- 15. Miscellaneous Topics in Regression. -- 15.1 Piecewise Regression and Alternatives. -- 15.2 Semiparametric Regression. -- 15.3 Quantile Regression. -- 15.4 Poisson Regression. -- 15.5 Negative Binomial Regression. -- 15.6 Cox Regression. -- 15.7 Probit Regression. -- 15.8 Censored Regression and Truncated Regression. -- 15.8.1 Tobit Regression. -- 15.9 Constrained Regression. -- 15.10 Interval Regression. -- 15.11 Random Coefficient Regression. -- 15.12 Partial Least Squares Regression. -- 15.13 Errors-in-Variables Regression. -- 15.14 Regression with Life Data. -- 15.15 Use of Regression in Survey Sampling. -- 15.16 Bayesian Regression. -- 15.17 Instrumental Variables Regression. -- 15.18 Shrinkage Estimators. -- 15.19 Meta-Regression. -- 15.20 Classification and Regression Trees (CART). -- 15.21 Multivariate Regression. -- References. -- Exercises. -- 16. Analysis of Real Data Sets. -- 16.1 Analyzing Buchanan's Presidential Vote in Palm Beach County in 2000. -- 16.2 Water Quality Data. -- 16.3 Predicting Lifespan? -- 16.4 Scottish Hill Races Data. -- 16.5 Leukemia Data. -- 16.6 Dosage Response Data. -- 16.7 A Strategy for Analyzing Regression Data. -- 16.8 Summary. -- References. -- Answers to Selected Exercises. -- Statistical Tables. -- Author Index. -- Subject Index.
Weitere Ausg.:
Print version: Ryan, Thomas P., 1945- Modern regression methods. Hoboken, N.J. : Wiley, ©2009 ISBN 9780470081860
Weitere Ausg.:
ISBN 0470081864
Sprache:
Englisch
Schlagwort(e):
Electronic books.
;
Electronic books.
;
Electronic books.
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470382806
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470382806
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470382806
