عنوان

Big and complex data analysis :methodologies and applications

Big and complex data analysis :methodologies and applications

Springer

2017

p.: ill

Contributions to statistics

Includes bibliographical references

S. Ejaz Ahmed, editor

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

، Big data

، Data mining

TI

AU Ahmed, S. E.)Syed Ejaz( editor 1957-

SE

SE Contributions to statistics