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Advanced Statistical Methods / / by Sahana Prasad
| Advanced Statistical Methods / / by Sahana Prasad |
| Autore | Prasad Sahana |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (238 pages) |
| Disciplina | 001.422 |
| Soggetto topico |
Statistics
Regression analysis Time-series analysis Statistical Theory and Methods Linear Models and Regression Time Series Analysis Estadística Anàlisi de regressió Anàlisi de sèries temporals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9789819972579
9819972574 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Advanced Concepts in Regression -- 2. Index Numbers -- 3. Time Series -- 4. Vital Statistics. |
| Record Nr. | UNINA-9910857789503321 |
Prasad Sahana
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Hosmer David W
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| Hoboken, N.J., : Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Minimum Gamma-Divergence for Regression and Classification Problems / / by Shinto Eguchi
| Minimum Gamma-Divergence for Regression and Classification Problems / / by Shinto Eguchi |
| Autore | Eguchi Shinto |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (212 pages) |
| Disciplina | 519.5 |
| Collana | JSS Research Series in Statistics |
| Soggetto topico |
Statistics
Stochastic models Mathematical statistics Machine learning Regression analysis Biometry Statistical Theory and Methods Stochastic Modelling in Statistics Parametric Inference Machine Learning Linear Models and Regression Biostatistics Estadística Estadística matemàtica Aprenentatge automàtic Anàlisi de regressió Biometria Models lineals (Estadística) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 9789819788804 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Framework of gamma-divergence -- 2.1. Scale invariance -- 2.2 GM divergence and HM divergence -- 3. Minimum divergence methods for generalized linear models -- 3.1. Bernoulli logistic model -- 3.2. Poisson log-linear model -- 3.3. Poisson point process model -- 4. Minimum divergence methods in machine leaning -- 4.1. Multi-class AdaBoost -- 4.2. Boltzmann machine -- 5. gamma-divergence for real valued functions -- 6. Discussion. |
| Record Nr. | UNINA-9910986137903321 |
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Eguchi Shinto
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling / / by George J. Knafl
| Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling / / by George J. Knafl |
| Autore | Knafl George J. |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (525 pages) |
| Disciplina | 780 |
| Soggetto topico |
Statistics
Biometry Statistical Theory and Methods Biostatistics Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031419881
303141988X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- About This Book -- Contents -- About the Author -- Abbreviations -- Chapter 1: Introduction -- 1.1 Background -- 1.2 Overview of Part I -- 1.3 Overview of Part II -- 1.4 Overview of Part III -- References -- Part I: Continuous, Count, and Dichotomous Outcomes -- Chapter 2: Standard GEE Modeling of Correlated Univariate Outcomes -- 2.1 Correlated Univariate Outcomes -- 2.2 Generalized Linear Modeling -- 2.2.1 Linear Regression with Identity Link Function -- 2.2.2 Poisson Regression with Natural Log Link Function -- 2.2.3 Logistic Regression with Logit Link Function -- 2.2.4 Exponential Regression with Natural Log Link Function -- 2.3 Modeling Correlations -- 2.3.1 Independent Correlations -- 2.3.2 Exchangeable Correlations -- 2.3.3 Autoregressive Order 1 Correlations -- 2.3.4 Unstructured Correlations -- 2.4 Standard GEE Modeling -- 2.4.1 Estimating the Correlation Structure -- 2.4.2 Estimating the Covariance Matrix for Mean Parameter Estimates -- 2.4.3 Parameter Estimation Problems -- 2.5 The Likelihood Function -- 2.6 Likelihood Cross-Validation -- 2.6.1 Choosing the Number of Folds -- 2.6.2 LCV Ratio Tests -- 2.6.3 Penalized Likelihood Criteria -- 2.7 Adaptive Regression Modeling of Means -- 2.8 Example Data Sets -- 2.8.1 The Dental Measurement Data -- 2.8.2 The Epilepsy Seizure Rate Data -- 2.8.3 The Dichotomous Respiratory Status Data -- 2.8.4 The Blood Lead Level Data -- References -- Chapter 3: Partially Modified GEE Modeling of Correlated Univariate Outcomes -- 3.1 Including Non-constant Dispersions -- 3.2 Adding Estimating Equations for the Dispersions Based on the Likelihood -- 3.3 Estimating the Correlation Structure -- 3.4 Estimating the Covariance Matrix for Coefficient Parameter Estimates -- 3.5 The Constant Dispersion Model -- 3.6 Degeneracy in Correlation Parameter Estimation.
3.7 The Estimation Process -- 3.7.1 Step 1 Adjustment -- 3.7.2 Step 2 Adjustment -- 3.7.3 Stopping the Estimation Process -- 3.7.4 Initial Estimates -- 3.7.5 Other Computational Issues -- 3.7.6 Recommended Tolerance Settings -- 3.8 Variation in Measurement Conditions -- References -- Chapter 4: Fully Modified GEE Modeling of Correlated Univariate Outcomes -- 4.1 Estimating Equations for Means and Dispersions Based on the Likelihood -- 4.2 Alternate Regression Types -- 4.2.1 Linear Regression with Identity Link Function -- 4.2.2 Poisson Regression with Natural Log Link Function -- 4.2.3 Logistic Regression with Logit Link Function -- 4.2.4 Exponential Regression with Natural Log Link Function -- 4.2.5 Inverse Gaussian Regression with Natural Log Link Function -- 4.3 The Parameter Estimation Process -- 4.3.1 Revised Stopping Criteria -- 4.3.2 Initial Estimates -- 4.4 Singleton Univariate Outcomes -- References -- Chapter 5: Extended Linear Mixed Modeling of Correlated Univariate Outcomes -- 5.1 Estimating Equations for Means, Dispersions, and Correlations Based on the Likelihood -- 5.2 Adjustments to the Estimation Process -- 5.3 Exchangeable Correlation Structure Computations -- 5.3.1 A General Class of Symmetric Matrices -- 5.3.2 Eigenvalues of the EC Correlation Matrix -- 5.3.3 Inverse of the EC Correlation Matrix -- 5.3.4 Square Root of the EC Correlation Matrix -- 5.3.5 Inverse of the Square Root of the EC Correlation Matrix -- 5.3.6 Derivatives with Respect to the Constant EC Correlation -- 5.4 Spatial Autoregressive Order 1 Correlation Structure Computations -- 5.4.1 Square Root and Determinant of the Spatial AR1 Correlation Matrix -- 5.4.2 Inverse of the Square Root of the Spatial AR1 Correlation Matrix -- 5.4.3 Derivatives with Respect to the Spatial Autocorrelation -- 5.5 Unstructured Correlation Structure Computations. 5.6 Verifying Gradient and Hessian Computations -- 5.7 Direct Variance Modeling -- References -- Chapter 6: Example Analyses of the Dental Measurement Data -- 6.1 Choosing the Number of Folds and the Correlation Structure -- 6.2 Assessing Linearity of Means in Child Age -- 6.3 Comparison to Standard GEE Modeling -- 6.4 Modeling Means and Variances in Child Age -- 6.5 Adaptive Additive Models in Child Age and Child Gender -- 6.6 Adaptive Moderation of the Effect of Child Age by Child Gender -- 6.7 Comparison to Standard Linear Moderation -- 6.8 Analysis Summary -- 6.9 Example SAS Code for Analyzing the Dental Measurement Data -- 6.9.1 Modeling Means in Child Age Assuming Constant Variances -- 6.9.2 Modeling Means and Variances in Child Age -- 6.9.3 Additive Models in Child Age and Child Gender -- 6.9.4 Moderation Models in Child Age and Child Gender -- 6.9.5 Example Output -- Reference -- Chapter 7: Example Analyses of the Epilepsy Seizure Rate Data -- 7.1 Choosing the Number of Folds and the Correlation Structure -- 7.2 Assessing Linearity of the Log of the Means in Visit -- 7.3 Comparison to Standard GEE Modeling -- 7.4 Modeling Means and Dispersions in Visit -- 7.5 Additive Models in Visit and Being in the Intervention Group -- 7.6 Adaptive Moderation of the Effect of Visit by Being in the Intervention Group -- 7.7 Comparison of Linear Additive and Moderation Models with Constant Dispersions -- 7.8 Direct Variance Modeling of Epilepsy Seizure Rates -- 7.9 Analysis Summary -- 7.10 Example SAS Code for Analyzing the Epilepsy Seizure Rate Data -- 7.10.1 Modeling Means in Visit Assuming Constant Dispersions -- 7.10.2 Modeling Means and Dispersions in Visit -- 7.10.3 Additive Models in Visit and Being in the Intervention Group -- 7.10.4 Moderation Models in Visit and Being in the Intervention Group -- 7.10.5 Direct Variance Modeling. 7.10.6 Example Output -- Reference -- Chapter 8: Example Analyses of the Dichotomous Respiratory Status Data -- 8.1 Choosing the Number of Folds and the Correlation Structure -- 8.2 Assessing Linearity of the Logits of the Means in Visit -- 8.3 Assessing Unit Versus Constant Dispersions -- 8.4 Comparison to Standard GEE Modeling -- 8.5 Modeling Means and Dispersions in Visit -- 8.6 Additive Models in Visit and Being on Active Treatment -- 8.7 Adaptive Moderation of the Effect of Visit by Being on Active Treatment -- 8.8 Comparison to Standard Linear Moderation -- 8.9 Direct Variance Modeling of Dichotomous Respiratory Status -- 8.10 Analysis Summary -- 8.11 Example SAS Code for Analyzing the Dichotomous Respiratory Status Data -- 8.11.1 Modeling Means in Visit Assuming Constant Dispersions -- 8.11.2 Modeling Means and Dispersions in Visit -- 8.11.3 Additive Models in Visit and Being on Active Treatment -- 8.11.4 Moderation Models in Visit and Being on Active Treatment -- 8.11.5 Direct Variance Modeling -- 8.11.6 Example Output -- Reference -- Chapter 9: Example Analyses of the Blood Lead Level Data -- 9.1 Choosing the Number of Folds and the Correlation Structure -- 9.2 Assessing Linearity of the Log of the Means in Week -- 9.3 Comparison to Standard GEE Modeling -- 9.4 Modeling Means and Dispersions in Week -- 9.5 Additive Models in Week and Being on Succimer -- 9.6 Adaptive Moderation of the Effect of Week by Being on Succimer -- 9.7 Direct Variance Modeling of Blood Lead Level Data -- 9.8 Analysis Summary -- 9.9 Example SAS Code for Analyzing the Blood Lead Level Data -- 9.9.1 Modeling Means in Week Assuming Constant Dispersions -- 9.9.2 Modeling Means and Dispersions in Week -- 9.9.3 Additive Models in Week and Being on Succimer -- 9.9.4 Moderation Models in Week and Being on Succimer -- 9.9.5 Direct Variance Modeling -- 9.9.6 Example Output. Reference -- Part II: Polytomous Outcomes -- Chapter 10: Multinomial Regression -- 10.1 Standard GEE Modeling -- 10.2 Partially and Fully Modified GEE Modeling -- 10.3 Alternate Correlation Structures -- 10.3.1 Independent Correlations -- 10.3.2 Exchangeable Correlations -- 10.3.3 Spatial Autoregressive Order 1 Correlations -- 10.3.4 Unstructured Correlations -- 10.3.5 Degeneracy in Correlation Estimates -- 10.4 Extended Linear Mixed Modeling -- 10.4.1 Estimating Equations for Means, Dispersions, and Correlations Based on the Likelihood -- 10.4.2 First Partial Derivatives with Respect to Mean Parameters -- 10.4.3 First Partial Derivatives with Respect to Correlation Parameters -- 10.4.4 Second Partial Derivatives with Respect to Mean Parameters -- 10.4.5 Second Partial Derivatives with Respect to Correlation Parameters -- 10.4.6 Second Partial Derivatives with Respect to Mean and Dispersion Parameters -- 10.4.7 Second Partial Derivatives with Respect to Mean and Correlation Parameters -- 10.4.8 Second Partial Derivatives with Respect to Dispersion and Correlation Parameters -- References -- Chapter 11: Ordinal Regression -- 11.1 Ordinal Regression Based on Individual Outcomes -- 11.1.1 Standard GEE Modeling -- 11.1.2 Partially and Fully Modified GEE Modeling -- 11.1.3 Alternate Correlation Structures -- 11.1.3.1 Independent Correlations -- 11.1.3.2 Exchangeable Correlations -- 11.1.3.3 Autoregressive Correlations -- 11.1.3.4 Unstructured Correlations -- 11.1.3.5 Degeneracy in Correlation Estimates -- 11.1.4 Extended Linear Mixed Modeling -- 11.1.4.1 Estimating Equations for Means, Dispersions, and Correlations Based on the Likelihood -- 11.1.4.2 First Partial Derivatives with Respect to Mean Parameters -- 11.1.4.3 First Partial Derivatives with Respect to Correlation Parameters -- 11.1.4.4 Second Partial Derivatives with Respect to Mean Parameters. 11.1.4.5 Second Partial Derivatives with Respect to Correlation Parameters. |
| Record Nr. | UNINA-9910805583103321 |
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Knafl George J.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Reinsel Gregory C.
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| New York, New York : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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 Methods for Environmental Mixtures : A Primer in Environmental Epidemiology / / by Andrea Bellavia
| Statistical Methods for Environmental Mixtures : A Primer in Environmental Epidemiology / / by Andrea Bellavia |
| Autore | Bellavia Andrea |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (148 pages) |
| Disciplina | 570.15195 |
| Collana | Society, Environment and Statistics |
| Soggetto topico |
Biometry
Statistics Regression analysis Biostatistics Bayesian Inference Linear Models and Regression Biometria Estadística Anàlisi de regressió |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031789878
3031789873 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Chapter 1 Environmental Mixtures -- Chapter 2 Characterizing Environmental Mixtures -- Chapter 3 Regression-Based Approaches for Mixture-Health Associations -- Chapter 4 Mixture Indexing Approaches -- Chapter 5 Flexible Approaches for Complex Settings -- Chapter 6 Additional Topics and Final Remarks. |
| Record Nr. | UNINA-9910983087203321 |
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Bellavia Andrea
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Chen Ding-Geng (Din)
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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. | UNINA-9910484649503321 |
|
O'Quigley John
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||
| Cham, Switzerland : , : 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. | UNISA-996466390803316 |
|
O'Quigley John
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
