Bing Li – författare

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field.Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The authorBing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) has been widely adopted as a routine analytical technique for elemental analysis in both industry and academia. However, spectral interference can be a major problem, particularly with such line-rich elements as the rare earth elements. An Atlas of High Resolution Spectra of Rare Earth Elements, which comes complete with a CD of spectra in full colour, is a reference source suitable for all analytical spectroscopists. Using some previously unpublished high resolution spectra, this atlas enables users of ICP-AES to select the best lines of any single rare earth element matrix. Clear instructions for the use of the accompanying CD are provided, which allows all adjacent interferent spectral profiles to be displayed and superimposed. Up-to-date and informative, this unique book will be welcomed as a practical and indispensable reference guide by all those who use ICP-AES for the analysis of rare earth elements.

Despite the accepted fact that a substantial part of the risk and return of any portfolio comes from asset allocation, we find today that the majority of investment professionals worldwide are focused on security selection. Multi-Asset Investing: A Practitioner’s Framework questions this basic structure of the investment process and investment industry. Who says we have to separate alpha and beta?Are the traditional definitions for risk and risk premium relevant in a multi-asset class world?Do portfolios cater for the ‘real risks’ in their investment processes?Does the whole Emerging Markets demarcation make sense for investing?Why do active Asian managers perform much poorer compared to developed market managers?Can you distinguish how much of a strategy’s performance comes from skill rather than luck?Does having a performance fee for your manager create alignment or misalignment?Why is the asset management transitioning from multi-asset strategies to multi-asset solutions?These and many other questions are asked, and suggestions provided as potential solutions. Having worked together for fifteen years, the authors’ present implementable solutions which have helped them successfully manage large asset pools.The Academic Perspective“Multi-Asset Investing asks fundamental questions about the asset allocation investment processes in use today, and can have a substantial impact on the future structure of the finance industry. It clarifies and distils the techniques that investment professionals need to master to add value to client portfolios.”—Paul Smith, President & CEO, CFA Institute“Pranay Gupta, Sven Skallsjo, and Bing Li describe the essential concepts and applications of multi-asset investing. Their treatment is far ranging and exceptionally lucid, and always with a nod to practical application. Buy this book and keep it close at hand.”—Mark Kritzman, MIT Sloane School of Management“Innovative solutions to some of the most difficult investment problems we are faced with today. Multi-asset Investing tackles investment issues which don’t have straight forward solutions, but nevertheless are faced by every investment professional. This book sets the standard for investment processes of all asset managers.” —SP Kothari, MIT Sloane School of ManagementThe Asset Owner Perspective“Multi-asset means different things to different people. This is the first text that details a comprehensive framework for managing any kind of multi-asset investment problem. Further, its explanation of the commercial aspects of managing a multi-asset investment business for an asset manager, private bank or asset owner make it an indispensable tool”—Sadayuki Horie, Dy. Chairman - Investment Advisory Comm., Government Pension Investment Fund, Japan“Multi-Asset Investing shows the substantial scope there is to innovate the asset allocation process. With its novel approaches to allocation, portfolio construction and risk management it demonstrates the substantial value that can be added to any portfolio. The solutions proposed by Multi-Asset Investing are creative, thought provoking, and may well be the way all portfolios need to be managed in the future.”—Mario Therrien, Senior Vice President, Caisse de Depot et Placement du Quebec, CanadaThe Asset Manager’s Perspective“Never has astute asset allocation and diversification been more crucial than today. Asset Managers which are able to innovate their investment processes and products in this area, are more likely to be the winners. Multi-Asset Investing provides both simple and sophisticated, tested and implementable techniques for successfully managing multi-asset portfolios.”—Vincent Camerlynck, former CEO BNP Paribas Investment Partners, Asia PacificThe Investment Strategist Perspective“For plan sponsors, portfolio managers, analysts and risk managers, Multi-Asset Investing is an unparalleled guide for portfolio management. Its approach to blending the quantitative and fundamental, top-down and bottom up and the risk and return frameworks makes it a valuable tool for any kind of investment professional. It clarifies a complex subject into a series of practical ideas to help add value to any portfolio.”—Ajay S. Kapur, Chief Strategist, BOA Merrill Lynch Asia

This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research.

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field.Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods.Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The authorBing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

In honor of professor and renowned statistician R. Dennis Cook, this festschrift explores his influential contributions to an array of statistical disciplines ranging from experimental design and population genetics, to statistical diagnostics and all areas of regression-related inference and analysis. Since the early 1990s, Prof. Cook has led the development of dimension reduction methodology in three distinct but related regression contexts: envelopes, sufficient dimension reduction (SDR), and regression graphics. In particular, he has made fundamental and pioneering contributions to SDR, inventing or co-inventing many popular dimension reduction methods, such as sliced average variance estimation, the minimum discrepancy approach, model-free variable selection, and sufficient dimension reduction subspaces.A prolific researcher and mentor, Prof. Cook is known for his ability to identify research problems in statistics that are both challenging and important, as well as his deep appreciation for the applied side of statistics. This collection of Prof. Cook's collaborators, colleagues, friends, and former students reflects the broad array of his contributions to the research and instructional arenas of statistics.

In honor of professor and renowned statistician R. Dennis Cook, this festschrift explores his influential contributions to an array of statistical disciplines ranging from experimental design and population genetics, to statistical diagnostics and all areas of regression-related inference and analysis. Since the early 1990s, Prof. Cook has led the development of dimension reduction methodology in three distinct but related regression contexts: envelopes, sufficient dimension reduction (SDR), and regression graphics. In particular, he has made fundamental and pioneering contributions to SDR, inventing or co-inventing many popular dimension reduction methods, such as sliced average variance estimation, the minimum discrepancy approach, model-free variable selection, and sufficient dimension reduction subspaces.A prolific researcher and mentor, Prof. Cook is known for his ability to identify research problems in statistics that are both challenging and important, as well as his deep appreciation for the applied side of statistics. This collection of Prof. Cook's collaborators, colleagues, friends, and former students reflects the broad array of his contributions to the research and instructional arenas of statistics.

They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

Parallel processing is a very important technique for improving the performance of various software development and maintenance activities. The purpose of this book is to introduce important techniques for parallel executation of high-level specifications of software systems. These techniques are very useful for the construction, analysis, and transformation of reliable large-scale and complex software systems.

Convergence in crossover service explores the crossover phenomenon, crossover services and the convergence issues that arise in typical crossover scenarios.