Big and complex data analysis :methodologies and applications
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
Springer
Date of Publication, Distribution, etc.
2017
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
p.: ill
SERIES
Other Title Information
Contributions to statistics
GENERAL NOTES
Text of Note
Includes bibliographical references
NOTES PERTAINING TO TITLE AND STATEMENT OF RESPONSIBILITY
Text of Note
S. Ejaz Ahmed, editor
CONTENTS NOTE
Text of Note
Preface; Contents; Part I General High-Dimensional Theory and Methods; Regularization After Marginal Learning for Ultra-High Dimensional Regression Models; 1 Introduction; 2 Model Setup and Several Methods in Variable Selection; 2.1 Model Setup and Notations; 2.2 Regularization Techniques; 2.3 Sure Independence Screening; 3 Regularization After Marginal Learning; 3.1 Algorithm; 3.2 Connections to SIS and RAR; 3.3 From RAM-2 to RAM; 4 Asymptotic Analysis; 4.1 Sure Independence Screening Property; 4.2 Sign Consistency for RAM-2; 4.3 Sign Consistency for RAM; 5 Numerical StudyPreface; Contents; Part I General High-Dimensional Theory and Methods; Regularization After Marginal Learning for Ultra-High Dimensional Regression Models; 1 Introduction; 2 Model Setup and Several Methods in Variable Selection; 2.1 Model Setup and Notations; 2.2 Regularization Techniques; 2.3 Sure Independence Screening; 3 Regularization After Marginal Learning; 3.1 Algorithm; 3.2 Connections to SIS and RAR; 3.3 From RAM-2 to RAM; 4 Asymptotic Analysis; 4.1 Sure Independence Screening Property; 4.2 Sign Consistency for RAM-2; 4.3 Sign Consistency for RAM; 5 Numerical StudyPreface; Contents; Part I General High-Dimensional Theory and Methods; Regularization After Marginal Learning for Ultra-High Dimensional Regression Models; 1 Introduction; 2 Model Setup and Several Methods in Variable Selection; 2.1 Model Setup and Notations; 2.2 Regularization Techniques; 2.3 Sure Independence Screening; 3 Regularization After Marginal Learning; 3.1 Algorithm; 3.2 Connections to SIS and RAR; 3.3 From RAM-2 to RAM; 4 Asymptotic Analysis; 4.1 Sure Independence Screening Property; 4.2 Sign Consistency for RAM-2; 4.3 Sign Consistency for RAM; 5 Numerical StudyPreface; Contents; Part I General High-Dimensional Theory and Methods; Regularization After Marginal Learning for Ultra-High Dimensional Regression Models; 1 Introduction; 2 Model Setup and Several Methods in Variable Selection; 2.1 Model Setup and Notations; 2.2 Regularization Techniques; 2.3 Sure Independence Screening; 3 Regularization After Marginal Learning; 3.1 Algorithm; 3.2 Connections to SIS and RAR; 3.3 From RAM-2 to RAM; 4 Asymptotic Analysis; 4.1 Sure Independence Screening Property; 4.2 Sign Consistency for RAM-2; 4.3 Sign Consistency for RAM; 5 Numerical Study