Linear regression analysis :: theory and computing /
This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Hackensack, N.J. :
World Scientific Pub. Co.,
©2009.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields. |
| Beschreibung: | 1 online resource (xix, 328 pages) : illustrations (some color) |
| Bibliographie: | Includes bibliographical references (pages 317-324) and index. |
| ISBN: | 9789812834119 9812834117 1282441698 9781282441699 9789814470087 9814470082 9786612441691 6612441690 |
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| 245 | 1 | 0 | |a Linear regression analysis : |b theory and computing / |c Xin Yan, Xiao Gang Su. |
| 260 | |a Singapore ; |a Hackensack, N.J. : |b World Scientific Pub. Co., |c ©2009. | ||
| 300 | |a 1 online resource (xix, 328 pages) : |b illustrations (some color) | ||
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| 504 | |a Includes bibliographical references (pages 317-324) and index. | ||
| 505 | 0 | |a 1. Introduction. 1.1. Regression model. 1.2. Goals of regression analysis. 1.3. Statistical computing in regression analysis -- 2. Simple linear regression. 2.1. Introduction. 2.2. Least squares estimation. 2.3. Statistical properties of the least squares estimation. 2.4. Maximum likelihood estimation. 2.5. Confidence interval on regression mean and regression prediction. 2.6. Statistical inference on regression parameters. 2.7. Residual analysis and model diagnosis. 2.8. Example -- 3. Multiple linear regression. 3.1. Vector space and projection. 3.2. Matrix form of multiple linear regression. 3.3. Quadratic form of random variables. 3.4. Idempotent matrices. 3.5. Multivariate normal distribution. 3.6. Quadratic form of the multivariate normal variables. 3.7. Least squares estimates of the multiple regression parameters. 3.8. Matrix form of the simple linear regression. 3.9. Test for full model and reduced model. 3.10. Test for general linear hypothesis. 3.11. The least squares estimates of multiple regression parameters under linear restrictions. 3.12. Confidence intervals of mean and prediction in multiple regression. 3.13. Simultaneous test for regression parameters. 3.14. Bonferroni confidence region for regression parameters. 3.15. Interaction and confounding. 3.16. Regression with dummy variables. 3.17. Collinearity in multiple linear regression. 3.18. Linear model in centered form. 3.19. Numerical computation of LSE via QR decomposition. 3.20. Analysis of regression residual. 3.21. Check for normality of the error term in multiple regression. 3.22. Example -- 4. Detection of outliers and influential observations in multiple linear regression. 4.1. Model diagnosis for multiple linear regression. 4.2. Detection of outliers in multiple linear regression. 4.3. Detection of influential observations in multiple linear regression. 4.4. Test for mean-shift outliers. 4.5. Graphical display of regression diagnosis. 4.6. Test for inferential observations. 4.7. Example -- 5. Model selection. 5.1. Effect of underfitting and overfitting. 5.2. All possible regressions. 5.3. Stepwise selection. 5.4. Examples. 5.5. Other related issues -- 6. Model diagnostics. 6.1. Test heteroscedasticity. 6.2. Detection of regression functional form -- 7. Extensions of least squares. 7.1. Non-full-rank linear regression models. 7.2. Generalized least squares. 7.3. Ridge regression and LASSO. 7.4. Parametric nonlinear regression -- 8. Generalized linear models. 8.1. Introduction: a motivating example. 8.2. Components of GLM. 8.3. Maximum likelihood estimation of GLM. 8.4. Statistical inference and other issues in GLM. 8.5. Logistic regression for binary data. 8.6. Poisson regression for count data -- 9. Bayesian linear regression. 9.1. Bayesian linear models. Bayesian model averaging. | |
| 520 | |a This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields. | ||
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 650 | 0 | |a Regression analysis. |0 http://id.loc.gov/authorities/subjects/sh85112392 | |
| 650 | 2 | |a Regression Analysis |0 https://id.nlm.nih.gov/mesh/D012044 | |
| 650 | 6 | |a Analyse de régression. | |
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| 650 | 7 | |a Regression analysis |2 fast | |
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| author | Yan, Xin, 1955- |
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| contents | 1. Introduction. 1.1. Regression model. 1.2. Goals of regression analysis. 1.3. Statistical computing in regression analysis -- 2. Simple linear regression. 2.1. Introduction. 2.2. Least squares estimation. 2.3. Statistical properties of the least squares estimation. 2.4. Maximum likelihood estimation. 2.5. Confidence interval on regression mean and regression prediction. 2.6. Statistical inference on regression parameters. 2.7. Residual analysis and model diagnosis. 2.8. Example -- 3. Multiple linear regression. 3.1. Vector space and projection. 3.2. Matrix form of multiple linear regression. 3.3. Quadratic form of random variables. 3.4. Idempotent matrices. 3.5. Multivariate normal distribution. 3.6. Quadratic form of the multivariate normal variables. 3.7. Least squares estimates of the multiple regression parameters. 3.8. Matrix form of the simple linear regression. 3.9. Test for full model and reduced model. 3.10. Test for general linear hypothesis. 3.11. The least squares estimates of multiple regression parameters under linear restrictions. 3.12. Confidence intervals of mean and prediction in multiple regression. 3.13. Simultaneous test for regression parameters. 3.14. Bonferroni confidence region for regression parameters. 3.15. Interaction and confounding. 3.16. Regression with dummy variables. 3.17. Collinearity in multiple linear regression. 3.18. Linear model in centered form. 3.19. Numerical computation of LSE via QR decomposition. 3.20. Analysis of regression residual. 3.21. Check for normality of the error term in multiple regression. 3.22. Example -- 4. Detection of outliers and influential observations in multiple linear regression. 4.1. Model diagnosis for multiple linear regression. 4.2. Detection of outliers in multiple linear regression. 4.3. Detection of influential observations in multiple linear regression. 4.4. Test for mean-shift outliers. 4.5. Graphical display of regression diagnosis. 4.6. Test for inferential observations. 4.7. Example -- 5. Model selection. 5.1. Effect of underfitting and overfitting. 5.2. All possible regressions. 5.3. Stepwise selection. 5.4. Examples. 5.5. Other related issues -- 6. Model diagnostics. 6.1. Test heteroscedasticity. 6.2. Detection of regression functional form -- 7. Extensions of least squares. 7.1. Non-full-rank linear regression models. 7.2. Generalized least squares. 7.3. Ridge regression and LASSO. 7.4. Parametric nonlinear regression -- 8. Generalized linear models. 8.1. Introduction: a motivating example. 8.2. Components of GLM. 8.3. Maximum likelihood estimation of GLM. 8.4. Statistical inference and other issues in GLM. 8.5. Logistic regression for binary data. 8.6. Poisson regression for count data -- 9. Bayesian linear regression. 9.1. Bayesian linear models. Bayesian model averaging. |
| ctrlnum | (OCoLC)613658550 |
| dewey-full | 519.536 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.536 |
| dewey-search | 519.536 |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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Co.,</subfield><subfield code="c">©2009.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xix, 328 pages) :</subfield><subfield code="b">illustrations (some color)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (pages 317-324) and index.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">1. Introduction. 1.1. Regression model. 1.2. 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Test for inferential observations. 4.7. Example -- 5. Model selection. 5.1. Effect of underfitting and overfitting. 5.2. All possible regressions. 5.3. Stepwise selection. 5.4. Examples. 5.5. Other related issues -- 6. Model diagnostics. 6.1. Test heteroscedasticity. 6.2. Detection of regression functional form -- 7. Extensions of least squares. 7.1. Non-full-rank linear regression models. 7.2. Generalized least squares. 7.3. Ridge regression and LASSO. 7.4. Parametric nonlinear regression -- 8. Generalized linear models. 8.1. Introduction: a motivating example. 8.2. Components of GLM. 8.3. Maximum likelihood estimation of GLM. 8.4. Statistical inference and other issues in GLM. 8.5. Logistic regression for binary data. 8.6. Poisson regression for count data -- 9. Bayesian linear regression. 9.1. Bayesian linear models. 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| id | ZDB-4-EBA-ocn613658550 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:11Z |
| institution | BVB |
| institution_GND | http://id.loc.gov/authorities/names/no2001005546 |
| isbn | 9789812834119 9812834117 1282441698 9781282441699 9789814470087 9814470082 9786612441691 6612441690 |
| language | English |
| oclc_num | 613658550 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xix, 328 pages) : illustrations (some color) |
| psigel | ZDB-4-EBA |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | World Scientific Pub. Co., |
| record_format | marc |
| spelling | Yan, Xin, 1955- https://id.oclc.org/worldcat/entity/E39PCjFTbP3kxGHkvtjD9QYdHy http://id.loc.gov/authorities/names/n2009018368 Linear regression analysis : theory and computing / Xin Yan, Xiao Gang Su. Singapore ; Hackensack, N.J. : World Scientific Pub. Co., ©2009. 1 online resource (xix, 328 pages) : illustrations (some color) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references (pages 317-324) and index. 1. Introduction. 1.1. Regression model. 1.2. Goals of regression analysis. 1.3. Statistical computing in regression analysis -- 2. Simple linear regression. 2.1. Introduction. 2.2. Least squares estimation. 2.3. Statistical properties of the least squares estimation. 2.4. Maximum likelihood estimation. 2.5. Confidence interval on regression mean and regression prediction. 2.6. Statistical inference on regression parameters. 2.7. Residual analysis and model diagnosis. 2.8. Example -- 3. Multiple linear regression. 3.1. Vector space and projection. 3.2. Matrix form of multiple linear regression. 3.3. Quadratic form of random variables. 3.4. Idempotent matrices. 3.5. Multivariate normal distribution. 3.6. Quadratic form of the multivariate normal variables. 3.7. Least squares estimates of the multiple regression parameters. 3.8. Matrix form of the simple linear regression. 3.9. Test for full model and reduced model. 3.10. Test for general linear hypothesis. 3.11. The least squares estimates of multiple regression parameters under linear restrictions. 3.12. Confidence intervals of mean and prediction in multiple regression. 3.13. Simultaneous test for regression parameters. 3.14. Bonferroni confidence region for regression parameters. 3.15. Interaction and confounding. 3.16. Regression with dummy variables. 3.17. Collinearity in multiple linear regression. 3.18. Linear model in centered form. 3.19. Numerical computation of LSE via QR decomposition. 3.20. Analysis of regression residual. 3.21. Check for normality of the error term in multiple regression. 3.22. Example -- 4. Detection of outliers and influential observations in multiple linear regression. 4.1. Model diagnosis for multiple linear regression. 4.2. Detection of outliers in multiple linear regression. 4.3. Detection of influential observations in multiple linear regression. 4.4. Test for mean-shift outliers. 4.5. Graphical display of regression diagnosis. 4.6. Test for inferential observations. 4.7. Example -- 5. Model selection. 5.1. Effect of underfitting and overfitting. 5.2. All possible regressions. 5.3. Stepwise selection. 5.4. Examples. 5.5. Other related issues -- 6. Model diagnostics. 6.1. Test heteroscedasticity. 6.2. Detection of regression functional form -- 7. Extensions of least squares. 7.1. Non-full-rank linear regression models. 7.2. Generalized least squares. 7.3. Ridge regression and LASSO. 7.4. Parametric nonlinear regression -- 8. Generalized linear models. 8.1. Introduction: a motivating example. 8.2. Components of GLM. 8.3. Maximum likelihood estimation of GLM. 8.4. Statistical inference and other issues in GLM. 8.5. Logistic regression for binary data. 8.6. Poisson regression for count data -- 9. Bayesian linear regression. 9.1. Bayesian linear models. Bayesian model averaging. This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields. Print version record. English. Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Regression Analysis https://id.nlm.nih.gov/mesh/D012044 Analyse de régression. MATHEMATICS Probability & Statistics Regression Analysis. bisacsh Regression analysis fast Su, Xiaogang, 1974- https://id.oclc.org/worldcat/entity/E39PCjFDYh4Yc3wg7fp4Bwj3PP http://id.loc.gov/authorities/names/n2009018370 World Scientific (Firm) http://id.loc.gov/authorities/names/no2001005546 9812834109 9789812834102 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=305216 Volltext |
| spellingShingle | Yan, Xin, 1955- Linear regression analysis : theory and computing / 1. Introduction. 1.1. Regression model. 1.2. Goals of regression analysis. 1.3. Statistical computing in regression analysis -- 2. Simple linear regression. 2.1. Introduction. 2.2. Least squares estimation. 2.3. Statistical properties of the least squares estimation. 2.4. Maximum likelihood estimation. 2.5. Confidence interval on regression mean and regression prediction. 2.6. Statistical inference on regression parameters. 2.7. Residual analysis and model diagnosis. 2.8. Example -- 3. Multiple linear regression. 3.1. Vector space and projection. 3.2. Matrix form of multiple linear regression. 3.3. Quadratic form of random variables. 3.4. Idempotent matrices. 3.5. Multivariate normal distribution. 3.6. Quadratic form of the multivariate normal variables. 3.7. Least squares estimates of the multiple regression parameters. 3.8. Matrix form of the simple linear regression. 3.9. Test for full model and reduced model. 3.10. Test for general linear hypothesis. 3.11. The least squares estimates of multiple regression parameters under linear restrictions. 3.12. Confidence intervals of mean and prediction in multiple regression. 3.13. Simultaneous test for regression parameters. 3.14. Bonferroni confidence region for regression parameters. 3.15. Interaction and confounding. 3.16. Regression with dummy variables. 3.17. Collinearity in multiple linear regression. 3.18. Linear model in centered form. 3.19. Numerical computation of LSE via QR decomposition. 3.20. Analysis of regression residual. 3.21. Check for normality of the error term in multiple regression. 3.22. Example -- 4. Detection of outliers and influential observations in multiple linear regression. 4.1. Model diagnosis for multiple linear regression. 4.2. Detection of outliers in multiple linear regression. 4.3. Detection of influential observations in multiple linear regression. 4.4. Test for mean-shift outliers. 4.5. Graphical display of regression diagnosis. 4.6. Test for inferential observations. 4.7. Example -- 5. Model selection. 5.1. Effect of underfitting and overfitting. 5.2. All possible regressions. 5.3. Stepwise selection. 5.4. Examples. 5.5. Other related issues -- 6. Model diagnostics. 6.1. Test heteroscedasticity. 6.2. Detection of regression functional form -- 7. Extensions of least squares. 7.1. Non-full-rank linear regression models. 7.2. Generalized least squares. 7.3. Ridge regression and LASSO. 7.4. Parametric nonlinear regression -- 8. Generalized linear models. 8.1. Introduction: a motivating example. 8.2. Components of GLM. 8.3. Maximum likelihood estimation of GLM. 8.4. Statistical inference and other issues in GLM. 8.5. Logistic regression for binary data. 8.6. Poisson regression for count data -- 9. Bayesian linear regression. 9.1. Bayesian linear models. Bayesian model averaging. Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Regression Analysis https://id.nlm.nih.gov/mesh/D012044 Analyse de régression. MATHEMATICS Probability & Statistics Regression Analysis. bisacsh Regression analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85112392 https://id.nlm.nih.gov/mesh/D012044 |
| title | Linear regression analysis : theory and computing / |
| title_auth | Linear regression analysis : theory and computing / |
| title_exact_search | Linear regression analysis : theory and computing / |
| title_full | Linear regression analysis : theory and computing / Xin Yan, Xiao Gang Su. |
| title_fullStr | Linear regression analysis : theory and computing / Xin Yan, Xiao Gang Su. |
| title_full_unstemmed | Linear regression analysis : theory and computing / Xin Yan, Xiao Gang Su. |
| title_short | Linear regression analysis : |
| title_sort | linear regression analysis theory and computing |
| title_sub | theory and computing / |
| topic | Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Regression Analysis https://id.nlm.nih.gov/mesh/D012044 Analyse de régression. MATHEMATICS Probability & Statistics Regression Analysis. bisacsh Regression analysis fast |
| topic_facet | Regression analysis. Regression Analysis Analyse de régression. MATHEMATICS Probability & Statistics Regression Analysis. Regression analysis |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=305216 |
| work_keys_str_mv | AT yanxin linearregressionanalysistheoryandcomputing AT suxiaogang linearregressionanalysistheoryandcomputing AT worldscientificfirm linearregressionanalysistheoryandcomputing |