Download or read book Joint Variable Selection for Data Envelopment Analysis Via Group Sparsity written by Zhiwei Qin and published by . This book was released on 2014 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study develops a data-driven group variable selection method for data envelopment analysis (DEA), a non-parametric linear programming approach to the estimation of production frontiers. The proposed method extends the group Lasso (least absolute shrinkage and selection operator) designed for variable selection on (often predefined) groups of variables in linear regression models to DEA models. In particular, a special constrained version of the group Lasso with the loss function suited for variable selection in DEA models is derived and solved by a new tailored algorithm based on the alternating direction method of multipliers (ADMM). This study further conducts a thorough evaluation of the proposed method against two widely used variable selection methods -- the efficiency contribution measure (ECM) method and the regression-based (RB) test -- in DEA via Monte Carlo simulations. The simulation results show that our method provides more favorable performance compared with its benchmarks.

Download or read book Optimization with Sparsity Inducing Penalties written by Francis Bach and published by . This book was released on 2011-12-23 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. Optimization with Sparsity-Inducing Penalties presents optimization tools and techniques dedicated to such sparsity-inducing penalties from a general perspective. It covers proximal methods, block-coordinate descent, reweighted ?2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provides an extensive set of experiments to compare various algorithms from a computational point of view. The presentation of Optimization with Sparsity-Inducing Penalties is essentially based on existing literature, but the process of constructing a general framework leads naturally to new results, connections and points of view. It is an ideal reference on the topic for anyone working in machine learning and related areas.

Download or read book Joint Species Distribution Modelling written by Otso Ovaskainen and published by Cambridge University Press. This book was released on 2020-06-11 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.

Download or read book Network Data Envelopment Analysis written by Chiang Kao and published by Springer. This book was released on 2016-08-23 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the underlying theory, model development, and applications of network Data Envelopment Analysis (DEA) in a systematic way. The field of network DEA extends and complements conventional DEA by considering not only inputs and outputs when measuring system efficiency, but also the internal structure of the system being analyzed. By analyzing the efficiency of individual internal components, and more particularly by studying the effects of relationships among components which are modeled and implemented by means of various network structures, the “network DEA” approach is able to help identify and manage the specific components that contribute inefficiencies into the overall systems. This relatively new approach comprises an important analytical tool based on mathematical programming techniques, with valuable implications to production and operations management. The existing models for measuring the efficiency of systems of specific network structures are also discussed, and the relationships between the system and component efficiencies are explored. This book should be able to inspire new research and new applications based on the current state of the art. Performance evaluation is an important task in management, and is needed to (i) better understand the past accomplishments of an organization and (ii) plan for its future development. However, this task becomes rather challenging when multiple performance metrics are involved. DEA is a powerful tool to cope with such issues. For systems or operations composed of interrelated processes, managers need to know how the performances of the various processes evaluated and how they are aggregated to form the overall performance of the system. This book provides an advanced exposition on performance evaluation of systems with network structures. It explores the network nature of most production and operation systems, and explains why network analyses are necessary.

Download or read book Introduction to Data Envelopment Analysis and Its Uses written by William W. Cooper and published by Springer Science & Business Media. This book was released on 2006-03-20 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References has been carefully designed by the authors to provide a systematic introduction to DEA and its uses as a multifaceted tool for evaluating problems in a variety of contexts. The authors have been involved in DEA's development from the beginning. William Cooper (with Abraham Charnes and Edwardo Rhodes) is a founder of DEA. Lawrence Seiford and Kaoru Tone have been actively involved as researchers and practitioners from its earliest beginnings. All have been deeply involved in uses of DEA in practical applications as well as in the development of its basic theory and methodologies. The result is a textbook grounded in authority, experience and substance.

Download or read book Multivariate Analysis of Ecological Data Using CANOCO written by Jan Lepš and published by Cambridge University Press. This book was released on 2003-05-29 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Table of contents

Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Download or read book An Introduction to Stochastic Modeling written by Howard M. Taylor and published by Academic Press. This book was released on 2014-05-10 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Download or read book An Introduction to Compressed Sensing written by M. Vidyasagar and published by SIAM. This book was released on 2019-12-03 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing is a relatively recent area of research that refers to the recovery of high-dimensional but low-complexity objects from a limited number of measurements. The topic has applications to signal/image processing and computer algorithms, and it draws from a variety of mathematical techniques such as graph theory, probability theory, linear algebra, and optimization. The author presents significant concepts never before discussed as well as new advances in the theory, providing an in-depth initiation to the field of compressed sensing. An Introduction to Compressed Sensing contains substantial material on graph theory and the design of binary measurement matrices, which is missing in recent texts despite being poised to play a key role in the future of compressed sensing theory. It also covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery. The book supplies relevant results alongside their proofs in a compact and streamlined presentation that is easy to navigate. The core audience for this book is engineers, computer scientists, and statisticians who are interested in compressed sensing. Professionals working in image processing, speech processing, or seismic signal processing will also find the book of interest.

Download or read book Data Preparation for Data Mining written by Dorian Pyle and published by Morgan Kaufmann. This book was released on 1999-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1990 with total page 1036 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation and Inference in Nonparametric Frontier Models written by Léopold Simar and published by Now Publishers. This book was released on 2013-05 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation and Inference in Nonparametric Frontier Models provides a thorough examination of this topic for students and researchers alike. Recent work has provided statistical properties of these estimators and methods for making statistical inference has established a link between frontier estimation and extreme value theory. New estimators that avoid many of the problems inherent with traditional efficiency estimators have been developed.

Download or read book Applied Spatial Data Analysis with R written by Roger S. Bivand and published by Springer Science & Business Media. This book was released on 2013-06-21 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

Download or read book Financial Signal Processing and Machine Learning written by Ali N. Akansu and published by John Wiley & Sons. This book was released on 2016-04-21 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Download or read book An Introduction to Efficiency and Productivity Analysis written by Timothy J. Coelli and published by Springer Science & Business Media. This book was released on 2005-07-22 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Softcover version of the second edition Hardcover. Incorporates a new author, Dr. Chris O'Donnell, who brings considerable expertise to the project in the area of performance measurement. Numerous topics are being added and more applications using real data, as well as exercises at the end of the chapters. Data sets, computer codes and software will be available for download from the web to accompany the volume.

Download or read book Basic Methods of Policy Analysis and Planning written by Carl Patton and published by Routledge. This book was released on 2015-08-26 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated in its 3rd edition, Basic Methods of Policy Analysis and Planning presents quickly applied methods for analyzing and resolving planning and policy issues at state, regional, and urban levels. Divided into two parts, Methods which presents quick methods in nine chapters and is organized around the steps in the policy analysis process, and Cases which presents seven policy cases, ranging in degree of complexity, the text provides readers with the resources they need for effective policy planning and analysis. Quantitative and qualitative methods are systematically combined to address policy dilemmas and urban planning problems. Readers and analysts utilizing this text gain comprehensive skills and background needed to impact public policy.

Download or read book Learning with Submodular Functions written by Francis Bach and published by . This book was released on 2013 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the Lovász extension of submodular functions provides a useful set of regularization functions for supervised and unsupervised learning. In this monograph, we present the theory of submodular functions from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems. In particular, we show how submodular function minimization is equivalent to solving a wide variety of convex optimization problems. This allows the derivation of new efficient algorithms for approximate and exact submodular function minimization with theoretical guarantees and good practical performance. By listing many examples of submodular functions, we review various applications to machine learning, such as clustering, experimental design, sensor placement, graphical model structure learning or subset selection, as well as a family of structured sparsity-inducing norms that can be derived and used from submodular functions.