Resve A. Saleh – författare

This work addresses the problems of simulating entire mixed analog/digital systems in the time-domain. A complete hierarchy of modelling and simulation methods for analog and digital circuits is described. It also provides a chronology of the research in the field of mixed-mode simulation and analog multilevel simulation over the last ten to 15 years. In addition, it provides enough information to the reader so that a prototype mixed-mode simulator could be developed using the algorithms in this book. The text can also be used as documentation for the SPLICE family of mixed-mode programs as they are based on the algorithms and techniques described in this book.

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning

Rank-Based Methods for Shrinkage and Selection

A practical and hands-on guide to the theory and methodology of statistical estimation based on rank

Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students.

Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes:

Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning

Rank-Based Methods for Shrinkage and Selection

A practical and hands-on guide to the theory and methodology of statistical estimation based on rank

Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students.

Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes:

Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning

An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problemsFundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

An essential guide for tackling outliers and anomalies in machine learning and data science.

In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few.

Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.

Fundamentals of Robust Machine Learning readers will also find:

A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problems

Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

An essential guide for tackling outliers and anomalies in machine learning and data science.

In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few.

Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.

Fundamentals of Robust Machine Learning readers will also find:

A blend of robust statistics and machine learning principlesDetailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detectionPython code with immediate application to data science problems

Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Mixed-Mode Simulation and Analog Multilevel Simulation addresses the problems of simulating entire mixed analog/digital systems in the time-domain. A complete hierarchy of modeling and simulation methods for analog and digital circuits is described. Mixed-Mode Simulation and Analog Multilevel Simulation also provides a chronology of the research in the field of mixed-mode simulation and analog multilevel simulation over the last ten to fifteen years. In addition, it provides enough information to the reader so that a prototype mixed-mode simulator could be developed using the algorithms in this book. Mixed-Mode Simulation and Analog Multilevel Simulation can also be used as documentation for the SPLICE family of mixed-mode programs as they are based on the algorithms and techniques described in this book.

Our purpose in writing this book was two-fold. First, we wanted to compile a chronology of the research in the field of mixed-mode simulation over the last ten to fifteen years. A substantial amount of work was done during this period of time but most of it was published in archival form in Masters theses and Ph. D. dissertations. Since the interest in mixed-mode simulation is growing, and a thorough review of the state-of-the-art in the area was not readily available, we thought it appropriate to publish the information in the form of a book. Secondly, we wanted to provide enough information to the reader so that a proto­ type mixed-mode simulator could be developed using the algorithms in this book. The SPLICE family of programs is based on the algorithms and techniques described in this book and so it can also serve as docu­ mentation for these programs. ACKNOWLEDGEMENTS The authors would like to dedicate this book to Prof. D. O. Peder­ son for inspiring this research work and for providing many years of support and encouragement The authors enjoyed many fruitful discus­ sions and collaborations with Jim Kleckner, Young Kim, Alberto Sangiovanni-Vincentelli, and Jacob White, and we thank them for their contributions. We also thank the countless others who participated in the research work and read early versions of this book. Lillian Beck provided many useful suggestions to improve the manuscript. Yun­ cheng Ju did the artwork for the illustrations.

Our purpose in writing this book was two-fold. First, we wanted to compile a chronology of the research in the field of mixed-mode simulation over the last ten to fifteen years. A substantial amount of work was done during this period of time but most of it was published in archival form in Masters theses and Ph. D. dissertations. Since the interest in mixed-mode simulation is growing, and a thorough review of the state-of-the-art in the area was not readily available, we thought it appropriate to publish the information in the form of a book. Secondly, we wanted to provide enough information to the reader so that a proto­ type mixed-mode simulator could be developed using the algorithms in this book. The SPLICE family of programs is based on the algorithms and techniques described in this book and so it can also serve as docu­ mentation for these programs. ACKNOWLEDGEMENTS The authors would like to dedicate this book to Prof. D. O. Peder­ son for inspiring this research work and for providing many years of support and encouragement The authors enjoyed many fruitful discus­ sions and collaborations with Jim Kleckner, Young Kim, Alberto Sangiovanni-Vincentelli, and Jacob White, and we thank them for their contributions. We also thank the countless others who participated in the research work and read early versions of this book. Lillian Beck provided many useful suggestions to improve the manuscript. Yun­ cheng Ju did the artwork for the illustrations.