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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 | ||
| ||
Multivariate reduced-rank regression : theory, methods and applications / / Gregory C. Reinsel, Rajabather Palani Velu, Kun Chen
| Multivariate reduced-rank regression : theory, methods and applications / / Gregory C. Reinsel, Rajabather Palani Velu, Kun Chen |
| Autore | Reinsel Gregory C. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | New York, New York : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (420 pages) : illustrations |
| Disciplina | 519.535 |
| Collana | Lecture notes in statistics |
| Soggetto topico |
Multivariate analysis
Regression analysis Anàlisi multivariable Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 1-0716-2793-7 |
| Classificazione | TKK |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface to the Second Edition -- Preface to the First Edition -- Contents -- About the Authors -- 1 Multivariate Linear Regression -- 1.1 Introduction -- 1.2 Multivariate Linear Regression Model and Least Squares Estimator -- 1.3 Further Inference Properties in the Multivariate Regression Model -- 1.4 Prediction in the Multivariate Linear Regression Model -- 1.5 Numerical Examples -- 1.5.1 Biochemical Data -- 1.5.2 Sales Performance Data -- 2 Reduced-Rank Regression Model -- 2.1 The Basic Reduced-Rank Model and Background -- 2.2 Some Examples of Application of the Reduced-Rank Model -- 2.3 Estimation of Parameters in the Reduced-Rank Model -- 2.4 Relation to Principal Components and Canonical Correlation Analysis -- 2.4.1 Principal Components Analysis -- 2.4.2 Application to Functional and Structural Relationships Models -- 2.4.3 Canonical Correlation Analysis -- 2.5 Asymptotic Distribution of Estimators in Reduced-Rank Model -- 2.6 Identification of Rank of the Regression Coefficient Matrix -- 2.7 Reduced-Rank Inverse Regression for Estimating Structural Dimension -- 2.8 Numerical Examples -- 2.9 Alternate Procedures for Analysis of Multivariate Regression Models -- 3 Reduced-Rank Regression Models with Two Sets of Regressors -- 3.1 Reduced-Rank Model of Anderson -- 3.2 Application to One-Way ANOVA and Linear Discriminant Analysis -- 3.3 Numerical Example Using Chemometrics Data -- 3.4 Both Regression Matrices of Lower Ranks: Model and Its Applications -- 3.5 Estimation and Inference for the Model -- 3.5.1 Efficient Estimator -- 3.5.2 An Alternative Estimator -- 3.5.3 Asymptotic Inference -- 3.6 Identification of Ranks of Coefficient Matrices -- 3.7 An Example on Ozone Data -- 3.8 Conclusion -- 4 Reduced-Rank Regression Model With Autoregressive Errors -- 4.1 Introduction and the Model.
4.2 Example on the U.K. Economy: Basic Data and Their Descriptions -- 4.3 Maximum Likelihood Estimators for the Model -- 4.4 Computational Algorithms for Efficient Estimators -- 4.5 Alternative Estimators and Their Properties -- 4.5.1 A Comparison Between Efficient and Other Estimators -- 4.6 Identification of Rank of the Regression Coefficient Matrix -- 4.7 Inference for the Numerical Example -- 4.8 An Alternate Estimator with Kronecker Approximation -- 4.8.1 Computational Results -- 5 Multiple Time Series Modeling With Reduced Ranks -- 5.1 Introduction and Time Series Models -- 5.2 Reduced-Rank Autoregressive Models -- 5.2.1 Estimation and Inference -- 5.2.2 Relationship to Canonical Analysis of Box and Tiao -- 5.3 An Extended Reduced-Rank Autoregressive Model -- 5.4 Nested Reduced-Rank Autoregressive Models -- 5.4.1 Specification of Ranks -- 5.4.2 A Canonical Form -- 5.4.3 Maximum Likelihood Estimation -- 5.5 Numerical Example: U.S. Hog Data -- 5.6 Relationship Between Nonstationarity and Canonical Correlations -- 5.7 Cointegration for Nonstationary Series-Reduced Rank in Long Term -- 5.7.1 LS and ML Estimation and Inference -- 5.7.2 Likelihood Ratio Test for the Number of Cointegrating Relations -- 5.8 Unit Root and Cointegration Aspects for the U.S. Hog Data Example -- 6 The Growth Curve Model and Reduced-Rank Regression Methods -- 6.1 Introduction and the Growth Curve Model -- 6.2 Estimation of Parameters in the Growth Curve Model -- 6.3 Likelihood Ratio Testing of Linear Hypotheses in Growth Curve Model -- 6.4 An Extended Model for Growth Curve Data -- 6.5 Modification of Basic Growth Curve Model to Reduced-Rank Model -- 6.6 Reduced-Rank Growth Curve Models -- 6.6.1 Extensions of the Reduced-Rank Growth Curve Model -- 6.7 Application to One-way ANOVA and Linear Discriminant Analysis -- 6.8 A Numerical Example -- 6.9 Some Recent Developments. 7 Seemingly Unrelated Regressions Models With Reduced Ranks -- 7.1 Introduction and the Seemingly Unrelated Regressions Model -- 7.2 Relation of Growth Curve Model to the Seemingly Unrelated Regressions Model -- 7.3 Reduced-Rank Coefficient in Seemingly Unrelated Regressions Model -- 7.4 Maximum Likelihood Estimators for Reduced-Rank Model -- 7.5 An Alternate Estimator and Its Properties -- 7.6 Identification of Rank of the Regression Coefficient Matrix -- 7.7 A Numerical Illustration with Scanner Data -- 7.8 Some Recent Developments -- 8 Applications of Reduced-Rank Regression in Financial Economics -- 8.1 Introduction to Asset Pricing Models -- 8.2 Estimation and Testing in the Asset Pricing Model -- 8.3 Additional Applications of Reduced-Rank Regression in Finance -- 8.4 Empirical Studies and Results on Asset Pricing Models -- 8.5 Related Topics -- 8.6 An Application -- 8.7 Cointegration and Pairs Trading -- 9 Partially Reduced-Rank Regression with Grouped Responses -- 9.1 Introduction: Partially Reduced-Rank Regression Model -- 9.2 Estimation of Parameters -- 9.3 Test for Rank and Inference Results -- 9.4 Procedures for Identification of Subset Reduced-Rank Structure -- 9.5 Illustrative Examples -- 9.6 Discussion and Extensions -- 10 High-Dimensional Reduced-Rank Regression -- 10.1 Introduction -- 10.2 Overview of High-Dimensional Regularized Regression -- 10.3 Framework of Singular Value Regularization -- 10.4 Reduced-Rank Regression via Adaptive Nuclear-Norm Penalization -- 10.4.1 Adaptive Nuclear Norm -- 10.4.2 Adaptive Nuclear-Norm Penalized Regression -- 10.4.3 Theoretical Analysis -- 10.4.3.1 Setup and Assumptions -- 10.4.3.2 Rank Consistency and Prediction Error Bound -- 10.5 Integrative Reduced-Rank Regression: Bridging Sparse and Low-Rank Models -- 10.5.1 Composite Nuclear-Norm Penalization -- 10.5.2 Theoretical Analysis. 10.6 Applications -- 10.6.1 Breast Cancer Data -- 10.6.2 Longitudinal Studies of Aging -- 11 Unbiased Risk Estimation in Reduced-Rank Regression -- 11.1 Introduction -- 11.2 Degrees of Freedom -- 11.3 Degrees of Freedom of Reduced-Rank Estimation -- 11.4 Comparing Empirical and Exact Estimators -- 11.4.1 Simulation Setup -- 11.4.2 Comparing Estimators of the Degrees of Freedom -- 11.4.3 Performance on Estimating the Prediction Error -- 11.4.4 Performance on Model Selection -- 11.5 Applications -- 11.5.1 Norwegian Paper Quality Data -- 11.5.2 Arabidopsis Thaliana Data -- 12 Generalized Reduced-Rank Regression -- 12.1 Introduction -- 12.2 Robust Reduced-Rank Regression -- 12.2.1 Non-Robustness of Reduced-Rank Regression -- 12.2.2 Robustification with Sparse Mean Shift -- 12.2.3 Theoretical Analysis -- 12.3 Reduced-Rank Estimation with Incomplete Data -- 12.3.1 Noiseless Matrix Completion -- 12.3.2 Stable Matrix Completion -- 12.3.3 Computation -- 12.4 Generalized Reduced-Rank Regression with Mixed Outcomes -- 12.5 Applications -- 12.5.1 Arabidopsis Thaliana data -- 12.5.2 Longitudinal Studies of Aging -- 13 Sparse Reduced-Rank Regression -- 13.1 Introduction -- 13.2 Sparse Reduced-Rank Regression -- 13.2.1 Sparse Reduced-Rank Regression for Predictor Selection -- 13.2.2 Computation -- 13.2.3 Theoretical Analysis -- 13.3 Co-sparse Factor Regression -- 13.3.1 Model Formulation and Deflation Procedures -- 13.3.2 Co-sparse Unit-Rank Estimation -- 13.3.3 Theoretical Analysis -- 13.4 Applications -- 13.4.1 Yeast eQTL Mapping Analysis -- 13.4.2 Forecasting Macroeconomic and Financial Indices -- Appendix -- References -- Subject Index -- Reference Index. |
| Record Nr. | UNINA-9910633916903321 |
|
Reinsel Gregory C.
|
||
| New York, New York : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multivariate reduced-rank regression : theory, methods and applications / / Gregory C. Reinsel, Rajabather Palani Velu, Kun Chen
| Multivariate reduced-rank regression : theory, methods and applications / / Gregory C. Reinsel, Rajabather Palani Velu, Kun Chen |
| Autore | Reinsel Gregory C. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | New York, New York : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (420 pages) : illustrations |
| Disciplina | 519.535 |
| Collana | Lecture notes in statistics |
| Soggetto topico |
Multivariate analysis
Regression analysis Anàlisi multivariable Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 1-0716-2793-7 |
| Classificazione | TKK |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface to the Second Edition -- Preface to the First Edition -- Contents -- About the Authors -- 1 Multivariate Linear Regression -- 1.1 Introduction -- 1.2 Multivariate Linear Regression Model and Least Squares Estimator -- 1.3 Further Inference Properties in the Multivariate Regression Model -- 1.4 Prediction in the Multivariate Linear Regression Model -- 1.5 Numerical Examples -- 1.5.1 Biochemical Data -- 1.5.2 Sales Performance Data -- 2 Reduced-Rank Regression Model -- 2.1 The Basic Reduced-Rank Model and Background -- 2.2 Some Examples of Application of the Reduced-Rank Model -- 2.3 Estimation of Parameters in the Reduced-Rank Model -- 2.4 Relation to Principal Components and Canonical Correlation Analysis -- 2.4.1 Principal Components Analysis -- 2.4.2 Application to Functional and Structural Relationships Models -- 2.4.3 Canonical Correlation Analysis -- 2.5 Asymptotic Distribution of Estimators in Reduced-Rank Model -- 2.6 Identification of Rank of the Regression Coefficient Matrix -- 2.7 Reduced-Rank Inverse Regression for Estimating Structural Dimension -- 2.8 Numerical Examples -- 2.9 Alternate Procedures for Analysis of Multivariate Regression Models -- 3 Reduced-Rank Regression Models with Two Sets of Regressors -- 3.1 Reduced-Rank Model of Anderson -- 3.2 Application to One-Way ANOVA and Linear Discriminant Analysis -- 3.3 Numerical Example Using Chemometrics Data -- 3.4 Both Regression Matrices of Lower Ranks: Model and Its Applications -- 3.5 Estimation and Inference for the Model -- 3.5.1 Efficient Estimator -- 3.5.2 An Alternative Estimator -- 3.5.3 Asymptotic Inference -- 3.6 Identification of Ranks of Coefficient Matrices -- 3.7 An Example on Ozone Data -- 3.8 Conclusion -- 4 Reduced-Rank Regression Model With Autoregressive Errors -- 4.1 Introduction and the Model.
4.2 Example on the U.K. Economy: Basic Data and Their Descriptions -- 4.3 Maximum Likelihood Estimators for the Model -- 4.4 Computational Algorithms for Efficient Estimators -- 4.5 Alternative Estimators and Their Properties -- 4.5.1 A Comparison Between Efficient and Other Estimators -- 4.6 Identification of Rank of the Regression Coefficient Matrix -- 4.7 Inference for the Numerical Example -- 4.8 An Alternate Estimator with Kronecker Approximation -- 4.8.1 Computational Results -- 5 Multiple Time Series Modeling With Reduced Ranks -- 5.1 Introduction and Time Series Models -- 5.2 Reduced-Rank Autoregressive Models -- 5.2.1 Estimation and Inference -- 5.2.2 Relationship to Canonical Analysis of Box and Tiao -- 5.3 An Extended Reduced-Rank Autoregressive Model -- 5.4 Nested Reduced-Rank Autoregressive Models -- 5.4.1 Specification of Ranks -- 5.4.2 A Canonical Form -- 5.4.3 Maximum Likelihood Estimation -- 5.5 Numerical Example: U.S. Hog Data -- 5.6 Relationship Between Nonstationarity and Canonical Correlations -- 5.7 Cointegration for Nonstationary Series-Reduced Rank in Long Term -- 5.7.1 LS and ML Estimation and Inference -- 5.7.2 Likelihood Ratio Test for the Number of Cointegrating Relations -- 5.8 Unit Root and Cointegration Aspects for the U.S. Hog Data Example -- 6 The Growth Curve Model and Reduced-Rank Regression Methods -- 6.1 Introduction and the Growth Curve Model -- 6.2 Estimation of Parameters in the Growth Curve Model -- 6.3 Likelihood Ratio Testing of Linear Hypotheses in Growth Curve Model -- 6.4 An Extended Model for Growth Curve Data -- 6.5 Modification of Basic Growth Curve Model to Reduced-Rank Model -- 6.6 Reduced-Rank Growth Curve Models -- 6.6.1 Extensions of the Reduced-Rank Growth Curve Model -- 6.7 Application to One-way ANOVA and Linear Discriminant Analysis -- 6.8 A Numerical Example -- 6.9 Some Recent Developments. 7 Seemingly Unrelated Regressions Models With Reduced Ranks -- 7.1 Introduction and the Seemingly Unrelated Regressions Model -- 7.2 Relation of Growth Curve Model to the Seemingly Unrelated Regressions Model -- 7.3 Reduced-Rank Coefficient in Seemingly Unrelated Regressions Model -- 7.4 Maximum Likelihood Estimators for Reduced-Rank Model -- 7.5 An Alternate Estimator and Its Properties -- 7.6 Identification of Rank of the Regression Coefficient Matrix -- 7.7 A Numerical Illustration with Scanner Data -- 7.8 Some Recent Developments -- 8 Applications of Reduced-Rank Regression in Financial Economics -- 8.1 Introduction to Asset Pricing Models -- 8.2 Estimation and Testing in the Asset Pricing Model -- 8.3 Additional Applications of Reduced-Rank Regression in Finance -- 8.4 Empirical Studies and Results on Asset Pricing Models -- 8.5 Related Topics -- 8.6 An Application -- 8.7 Cointegration and Pairs Trading -- 9 Partially Reduced-Rank Regression with Grouped Responses -- 9.1 Introduction: Partially Reduced-Rank Regression Model -- 9.2 Estimation of Parameters -- 9.3 Test for Rank and Inference Results -- 9.4 Procedures for Identification of Subset Reduced-Rank Structure -- 9.5 Illustrative Examples -- 9.6 Discussion and Extensions -- 10 High-Dimensional Reduced-Rank Regression -- 10.1 Introduction -- 10.2 Overview of High-Dimensional Regularized Regression -- 10.3 Framework of Singular Value Regularization -- 10.4 Reduced-Rank Regression via Adaptive Nuclear-Norm Penalization -- 10.4.1 Adaptive Nuclear Norm -- 10.4.2 Adaptive Nuclear-Norm Penalized Regression -- 10.4.3 Theoretical Analysis -- 10.4.3.1 Setup and Assumptions -- 10.4.3.2 Rank Consistency and Prediction Error Bound -- 10.5 Integrative Reduced-Rank Regression: Bridging Sparse and Low-Rank Models -- 10.5.1 Composite Nuclear-Norm Penalization -- 10.5.2 Theoretical Analysis. 10.6 Applications -- 10.6.1 Breast Cancer Data -- 10.6.2 Longitudinal Studies of Aging -- 11 Unbiased Risk Estimation in Reduced-Rank Regression -- 11.1 Introduction -- 11.2 Degrees of Freedom -- 11.3 Degrees of Freedom of Reduced-Rank Estimation -- 11.4 Comparing Empirical and Exact Estimators -- 11.4.1 Simulation Setup -- 11.4.2 Comparing Estimators of the Degrees of Freedom -- 11.4.3 Performance on Estimating the Prediction Error -- 11.4.4 Performance on Model Selection -- 11.5 Applications -- 11.5.1 Norwegian Paper Quality Data -- 11.5.2 Arabidopsis Thaliana Data -- 12 Generalized Reduced-Rank Regression -- 12.1 Introduction -- 12.2 Robust Reduced-Rank Regression -- 12.2.1 Non-Robustness of Reduced-Rank Regression -- 12.2.2 Robustification with Sparse Mean Shift -- 12.2.3 Theoretical Analysis -- 12.3 Reduced-Rank Estimation with Incomplete Data -- 12.3.1 Noiseless Matrix Completion -- 12.3.2 Stable Matrix Completion -- 12.3.3 Computation -- 12.4 Generalized Reduced-Rank Regression with Mixed Outcomes -- 12.5 Applications -- 12.5.1 Arabidopsis Thaliana data -- 12.5.2 Longitudinal Studies of Aging -- 13 Sparse Reduced-Rank Regression -- 13.1 Introduction -- 13.2 Sparse Reduced-Rank Regression -- 13.2.1 Sparse Reduced-Rank Regression for Predictor Selection -- 13.2.2 Computation -- 13.2.3 Theoretical Analysis -- 13.3 Co-sparse Factor Regression -- 13.3.1 Model Formulation and Deflation Procedures -- 13.3.2 Co-sparse Unit-Rank Estimation -- 13.3.3 Theoretical Analysis -- 13.4 Applications -- 13.4.1 Yeast eQTL Mapping Analysis -- 13.4.2 Forecasting Macroeconomic and Financial Indices -- Appendix -- References -- Subject Index -- Reference Index. |
| Record Nr. | UNISA-996499866803316 |
|
Reinsel Gregory C.
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| New York, New York : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Regression : models, methods and applications / / edited by Ludwig Fahrmeir [and three others]
| Regression : models, methods and applications / / edited by Ludwig Fahrmeir [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (757 pages) |
| Disciplina | 519.536 |
| Soggetto topico |
Regression analysis
Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-63882-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910552734003321 |
| Berlin, Germany : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Regression : models, methods and applications / / edited by Ludwig Fahrmeir [and three others]
| Regression : models, methods and applications / / edited by Ludwig Fahrmeir [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (757 pages) |
| Disciplina | 519.536 |
| Soggetto topico |
Regression analysis
Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-63882-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466553403316 |
| Berlin, Germany : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Regression Modeling with R [[electronic resource] ] : Longitudinal and Multi-level Modeling / / by Ding-Geng (Din) Chen, Jenny K. Chen
| Statistical Regression Modeling with R [[electronic resource] ] : Longitudinal and Multi-level Modeling / / by Ding-Geng (Din) Chen, Jenny K. Chen |
| Autore | Chen Ding-Geng (Din) |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (239 pages) |
| Disciplina | 519.536 |
| Collana | Emerging Topics in Statistics and Biostatistics |
| Soggetto topico |
Statistics
Programming languages (Electronic computers) Statistical Theory and Methods Applied Statistics Programming Language Anàlisi de regressió R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-67583-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Linear Regression -- 2. Introduction to Multi-Level Regression -- 3. Two-Level Multi-Level Modeling -- 4. Higher-Level Multi-Level Modeling -- 5. Longitudinal Data Analysis -- 6. Nonlinear Regression Modeling -- 7. Nonlinear Mixed-Effects Modeling -- 8. Generalized Linear Model -- 9. Generalized Multi-Level Model for Dichotomous Outcome -- 10. Generalized Multi-Level Model for Counts Outcome. |
| Record Nr. | UNISA-996466552203316 |
|
Chen Ding-Geng (Din)
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Regression Modeling with R : Longitudinal and Multi-level Modeling / / by Ding-Geng (Din) Chen, Jenny K. Chen
| Statistical Regression Modeling with R : Longitudinal and Multi-level Modeling / / by Ding-Geng (Din) Chen, Jenny K. Chen |
| Autore | Chen Ding-Geng (Din) |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (239 pages) |
| Disciplina | 519.536 |
| Collana | Emerging Topics in Statistics and Biostatistics |
| Soggetto topico |
Statistics
Programming languages (Electronic computers) Statistical Theory and Methods Applied Statistics Programming Language Anàlisi de regressió R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-67583-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Linear Regression -- 2. Introduction to Multi-Level Regression -- 3. Two-Level Multi-Level Modeling -- 4. Higher-Level Multi-Level Modeling -- 5. Longitudinal Data Analysis -- 6. Nonlinear Regression Modeling -- 7. Nonlinear Mixed-Effects Modeling -- 8. Generalized Linear Model -- 9. Generalized Multi-Level Model for Dichotomous Outcome -- 10. Generalized Multi-Level Model for Counts Outcome. |
| Record Nr. | UNINA-9910483312403321 |
|
Chen Ding-Geng (Din)
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Survival analysis proportional and non-proportional hazards regression / / John O'Quigley
| Survival analysis proportional and non-proportional hazards regression / / John O'Quigley |
| Autore | O'Quigley John |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (476 pages) |
| Disciplina | 363.17 |
| Soggetto topico |
Hazardous substances - Risk assessment
Regression analysis Substàncies perilloses Avaluació del risc Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-33439-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Summary of main notation -- 1 Introduction -- 1.1 Chapter summary -- 1.2 Context and motivation -- 1.3 Some examples -- 1.4 Main objectives -- 1.5 Neglected and underdeveloped topics -- 1.6 Model-based prediction -- 1.7 Data sets -- 1.8 Use as a graduate text -- 1.9 Classwork and homework -- 2 Survival analysis methodology -- 2.1 Chapter summary -- 2.2 Context and motivation -- 2.3 Basic tools -- 2.4 Some potential models -- 2.5 Censoring -- 2.6 Competing risks -- 2.7 Classwork and homework -- 3 Survival without covariates -- 3.1 Chapter summary -- 3.2 Context and motivation -- 3.3 Parametric models for survival functions -- 3.4 Empirical estimate (no censoring) -- 3.5 Kaplan-Meier (empirical estimate with censoring) -- 3.6 Nelson-Aalen estimate of survival -- 3.7 Model verification using empirical estimate -- 3.8 Classwork and homework -- 3.9 Outline of proofs -- 4 Proportional hazards models -- 4.1 Chapter summary -- 4.2 Context and motivation -- 4.3 General or non-proportional hazards model -- 4.4 Proportional hazards model -- 4.5 Cox regression model -- 4.6 Modeling multivariate problems -- 4.7 Classwork and homework -- 5 Proportional hazards models in epidemiology -- 5.1 Chapter summary -- 5.2 Context and motivation -- 5.3 Odds ratio, relative risk, and 2times2 tables -- 5.4 Logistic regression and proportional hazards -- 5.5 Survival in specific groups -- 5.6 Genetic epidemiology -- 5.7 Classwork and homework -- 6 Non-proportional hazards models -- 6.1 Chapter summary -- 6.2 Context and motivation -- 6.3 Partially proportional hazards models -- 6.4 Partitioning of the time axis -- 6.5 Time-dependent covariates -- 6.6 Linear and alternative model formulations -- 6.7 Classwork and homework -- 7 Model-based estimating equations -- 7.1 Chapter summary -- 7.2 Context and motivation.
7.3 Likelihood solution for parametric models -- 7.4 Semi-parametric estimating equations -- 7.5 Estimating equations using moments -- 7.6 Incorrectly specified models -- 7.7 Estimating equations in small samples -- 7.8 Classwork and homework -- 7.9 Outline of proofs -- 8 Survival given covariate information -- 8.1 Chapter summary -- 8.2 Context and motivation -- 8.3 Probability that Ti is greater than Tj -- 8.4 Conditional survival given ZinH -- 8.5 Other relative risk forms -- 8.6 Informative censoring -- 8.7 Classwork and homework -- 8.8 Outline of proofs -- 9 Regression effect process -- 9.1 Chapter summary -- 9.2 Context and motivation -- 9.3 Elements of the regression effect process -- 9.4 Univariate regression effect process -- 9.5 Regression effect processes for several covariates -- 9.6 Iterated logarithm for effective sample size -- 9.7 Classwork and homework -- 9.8 Outline of proofs -- 10 Model construction guided by regression effect process -- 10.1 Chapter summary -- 10.2 Context and motivation -- 10.3 Classical graphical methods -- 10.4 Confidence bands for regression effect process -- 10.5 Structured tests for time dependency -- 10.6 Predictive ability of a regression model -- 10.7 The R2 estimate of Ω2 -- 10.8 Using R2 and fit to build models -- 10.9 Some simulated situations -- 10.10 Illustrations from clinical studies -- 10.11 Classwork and homework -- 10.12 Outline of proofs -- 11 Hypothesis tests based on regression effect process -- 11.1 Chapter summary -- 11.2 Context and motivation -- 11.3 Some commonly employed tests -- 11.4 Tests based on the regression effect process -- 11.5 Choosing the best test statistic -- 11.6 Relative efficiency of competing tests -- 11.7 Supremum tests over cutpoints -- 11.8 Some simulated comparisons -- 11.9 Illustrations -- 11.10 Some further thoughts -- 11.11 Classwork and homework. 11.12 Outline of proofs -- A Probability -- A.1 Essential tools for survival problems -- A.2 Integration and measure -- A.3 Random variables and probability measure -- A.4 Convergence for random variables -- A.5 Topology and distance measures -- A.6 Distributions and densities -- A.7 Multivariate and copula models -- A.8 Expectation -- A.9 Order statistics and their expectations -- A.10 Approximations -- B Stochastic processes -- B.1 Broad overview -- B.2 Brownian motion -- B.3 Counting processes and martingales -- B.4 Inference for martingales and stochastic integrals -- C Limit theorems -- C.1 Empirical processes and central limit theorems -- C.2 Limit theorems for sums of random variables -- C.3 Functional central limit theorem -- C.4 Brownian motion as limit process -- C.5 Empirical distribution function -- D Inferential tools -- D.1 Theory of estimating equations -- D.2 Efficiency in estimation and in tests -- D.3 Inference using resampling techniques -- D.4 Conditional, marginal, and partial likelihood -- E Simulating data under the non-proportional hazards model -- E.1 Method 1-Change-point models -- E.2 Method 2-Non-proportional hazards models -- Further exercises and proofs -- Bibliography -- Index. |
| Record Nr. | UNINA-9910484649503321 |
|
O'Quigley John
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Survival analysis proportional and non-proportional hazards regression / / John O'Quigley
| Survival analysis proportional and non-proportional hazards regression / / John O'Quigley |
| Autore | O'Quigley John |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (476 pages) |
| Disciplina | 363.17 |
| Soggetto topico |
Hazardous substances - Risk assessment
Regression analysis Substàncies perilloses Avaluació del risc Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-33439-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Summary of main notation -- 1 Introduction -- 1.1 Chapter summary -- 1.2 Context and motivation -- 1.3 Some examples -- 1.4 Main objectives -- 1.5 Neglected and underdeveloped topics -- 1.6 Model-based prediction -- 1.7 Data sets -- 1.8 Use as a graduate text -- 1.9 Classwork and homework -- 2 Survival analysis methodology -- 2.1 Chapter summary -- 2.2 Context and motivation -- 2.3 Basic tools -- 2.4 Some potential models -- 2.5 Censoring -- 2.6 Competing risks -- 2.7 Classwork and homework -- 3 Survival without covariates -- 3.1 Chapter summary -- 3.2 Context and motivation -- 3.3 Parametric models for survival functions -- 3.4 Empirical estimate (no censoring) -- 3.5 Kaplan-Meier (empirical estimate with censoring) -- 3.6 Nelson-Aalen estimate of survival -- 3.7 Model verification using empirical estimate -- 3.8 Classwork and homework -- 3.9 Outline of proofs -- 4 Proportional hazards models -- 4.1 Chapter summary -- 4.2 Context and motivation -- 4.3 General or non-proportional hazards model -- 4.4 Proportional hazards model -- 4.5 Cox regression model -- 4.6 Modeling multivariate problems -- 4.7 Classwork and homework -- 5 Proportional hazards models in epidemiology -- 5.1 Chapter summary -- 5.2 Context and motivation -- 5.3 Odds ratio, relative risk, and 2times2 tables -- 5.4 Logistic regression and proportional hazards -- 5.5 Survival in specific groups -- 5.6 Genetic epidemiology -- 5.7 Classwork and homework -- 6 Non-proportional hazards models -- 6.1 Chapter summary -- 6.2 Context and motivation -- 6.3 Partially proportional hazards models -- 6.4 Partitioning of the time axis -- 6.5 Time-dependent covariates -- 6.6 Linear and alternative model formulations -- 6.7 Classwork and homework -- 7 Model-based estimating equations -- 7.1 Chapter summary -- 7.2 Context and motivation.
7.3 Likelihood solution for parametric models -- 7.4 Semi-parametric estimating equations -- 7.5 Estimating equations using moments -- 7.6 Incorrectly specified models -- 7.7 Estimating equations in small samples -- 7.8 Classwork and homework -- 7.9 Outline of proofs -- 8 Survival given covariate information -- 8.1 Chapter summary -- 8.2 Context and motivation -- 8.3 Probability that Ti is greater than Tj -- 8.4 Conditional survival given ZinH -- 8.5 Other relative risk forms -- 8.6 Informative censoring -- 8.7 Classwork and homework -- 8.8 Outline of proofs -- 9 Regression effect process -- 9.1 Chapter summary -- 9.2 Context and motivation -- 9.3 Elements of the regression effect process -- 9.4 Univariate regression effect process -- 9.5 Regression effect processes for several covariates -- 9.6 Iterated logarithm for effective sample size -- 9.7 Classwork and homework -- 9.8 Outline of proofs -- 10 Model construction guided by regression effect process -- 10.1 Chapter summary -- 10.2 Context and motivation -- 10.3 Classical graphical methods -- 10.4 Confidence bands for regression effect process -- 10.5 Structured tests for time dependency -- 10.6 Predictive ability of a regression model -- 10.7 The R2 estimate of Ω2 -- 10.8 Using R2 and fit to build models -- 10.9 Some simulated situations -- 10.10 Illustrations from clinical studies -- 10.11 Classwork and homework -- 10.12 Outline of proofs -- 11 Hypothesis tests based on regression effect process -- 11.1 Chapter summary -- 11.2 Context and motivation -- 11.3 Some commonly employed tests -- 11.4 Tests based on the regression effect process -- 11.5 Choosing the best test statistic -- 11.6 Relative efficiency of competing tests -- 11.7 Supremum tests over cutpoints -- 11.8 Some simulated comparisons -- 11.9 Illustrations -- 11.10 Some further thoughts -- 11.11 Classwork and homework. 11.12 Outline of proofs -- A Probability -- A.1 Essential tools for survival problems -- A.2 Integration and measure -- A.3 Random variables and probability measure -- A.4 Convergence for random variables -- A.5 Topology and distance measures -- A.6 Distributions and densities -- A.7 Multivariate and copula models -- A.8 Expectation -- A.9 Order statistics and their expectations -- A.10 Approximations -- B Stochastic processes -- B.1 Broad overview -- B.2 Brownian motion -- B.3 Counting processes and martingales -- B.4 Inference for martingales and stochastic integrals -- C Limit theorems -- C.1 Empirical processes and central limit theorems -- C.2 Limit theorems for sums of random variables -- C.3 Functional central limit theorem -- C.4 Brownian motion as limit process -- C.5 Empirical distribution function -- D Inferential tools -- D.1 Theory of estimating equations -- D.2 Efficiency in estimation and in tests -- D.3 Inference using resampling techniques -- D.4 Conditional, marginal, and partial likelihood -- E Simulating data under the non-proportional hazards model -- E.1 Method 1-Change-point models -- E.2 Method 2-Non-proportional hazards models -- Further exercises and proofs -- Bibliography -- Index. |
| Record Nr. | UNISA-996466390803316 |
|
O'Quigley John
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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