This Festschrift in honour of Ursula Gather’s 60th birthday deals with modern topics in the field of robust statistical methods, especially for time series and regression analysis, and with statistical methods for complex data structures. The individual contributions of leading experts provide a textbook-style overview of the topic, supplemented by current research results and questions. The statistical theory and methods in this volume aim at the analysis of data which deviate from classical stringent model assumptions, which contain outlying values and/or have a complex structure. Written for researchers as well as master and PhD students with a good knowledge of statistics.
Estimation of Stochastic Processes is intended for researchers in the field of econometrics, financial mathematics, statistics or signal processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It focuses on the estimation of functionals of unobserved values for stochastic processes with stationary increments, including ARIMA processes, seasonal time series and a class of cointegrated sequences. Furthermore, this book presents solutions to extrapolation (forecast), interpolation (missed values estimation) and filtering (smoothing) problems based on observations with and without noise, in discrete and continuous time domains. Extending the classical approach applied when the spectral densities of the processes are known, the minimax method of estimation is developed for a case where the spectral information is incomplete and the relations that determine the least favorable spectral densities for the optimal estimations are found.
"Crafting Data-Driven Solutions: Core Principles for Robust, Scalable, and Sustainable Systems" stands as an essential resource for anyone tasked with developing, scaling, or managing applications where data is the pivotal component. This comprehensive guide delves into the architectural frameworks of data systems, elucidating the intricate interactions that influence performance, scalability, and reliability. From foundational principles to advanced theories in batch and stream processing, each chapter systematically unveils critical insights into data models, storage solutions, encoding techniques, replication strategies, and more, supported by real-world case studies and practical examples. Whether you are an experienced software architect, a developer keen to enhance your data system design skills, or a student preparing to navigate the complexities of big data, this book provides the deep knowledge and expert guidance needed to excel in the realm of data-driven systems. Empower yourself with the expertise to design resilient architectures that not only fulfill operational requirements but also scale gracefully with evolving data demands. Make "Crafting Data-Driven Solutions" your go-to reference for building next-generation systems that are robust, efficient, and sustainable.
The Handbook of Discrete and Computational Geometry is intended as a reference book fully accessible to nonspecialists as well as specialists, covering all major aspects of both fields. The book offers the most important results and methods in discrete and computational geometry to those who use them in their work, both in the academic world—as researchers in mathematics and computer science—and in the professional world—as practitioners in fields as diverse as operations research, molecular biology, and robotics. Discrete geometry has contributed significantly to the growth of discrete mathematics in recent years. This has been fueled partly by the advent of powerful computers and by the recent explosion of activity in the relatively young field of computational geometry. This synthesis between discrete and computational geometry lies at the heart of this Handbook. A growing list of application fields includes combinatorial optimization, computer-aided design, computer graphics, crystallography, data analysis, error-correcting codes, geographic information systems, motion planning, operations research, pattern recognition, robotics, solid modeling, and tomography.
This book results from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) in June 2007 in Girona, Spain. This workshop was held alongside the 3rd Iberian Conference on Pattern Recognition and Image Analysis.
This Volume contains the Keynote, Invited and Full Contributed papers presented at COMPSTAT 2000. A companion volume (Jansen & Bethlehem, 2000) contains papers describing the Short Communications and Posters. COMPST AT is a one week conference held every two years under the auspices of the International Association of Statistical Computing, a section of the International Statistical Institute. COMPST AT 2000 is jointly organised by the Department of Methodology and Statistics of the Faculty of Social Sciences of Utrecht University, and Statistics Netherlands. It is taking place from 21-25 August 2000 at Utrecht University. Previous COMPSTATs (from 1974-1998) were in Vienna, Berlin, Leiden, Edinburgh, Toulouse, Prague, Rome, Copenhagen, Dubrovnik, Neuchatel, Vienna, Barcelona and Bristol. The conference is the main European forum for developments at the interface between statistics and computing. This was encapsulated as follows on the COMPST A T 2000 homepage http://neon. vb.cbs.nlIrsml compstat. Statistical computing provides the link between statistical theory and applied statistics. As at previous COMPSTATs, the scientific programme will range over all aspects of this link, from the development and implementation of new statistical ideas through to user experiences and software evaluation. The programme should appeal to anyone working in statistics and using computers, whether in universities, industrial companies, research institutes or as software developers. At COMPST AT 2000 there is a special interest in the interplay with official statistics. This is evident from papers in the area of computerised data collection, survey methodology, treatment of missing data, and the like.
Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applications Key Features Explore causal analysis with hands-on R tutorials and real-world examples Grasp complex statistical methods by taking a detailed, easy-to-follow approach Equip yourself with actionable insights and strategies for making data-driven decisions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDetermining causality in data is difficult due to confounding factors. Written by an applied scientist specializing in causal inference with over a decade of experience, Causal Inference in R provides the tools and methods you need to accurately establish causal relationships, improving data-driven decision-making. This book helps you get to grips with foundational concepts, offering a clear understanding of causal models and their relevance in data analysis. You’ll progress through chapters that blend theory with hands-on examples, illustrating how to apply advanced statistical methods to real-world scenarios. You’ll discover techniques for establishing causality, from classic approaches to contemporary methods, such as propensity score matching and instrumental variables. Each chapter is enriched with detailed case studies and R code snippets, enabling you to implement concepts immediately. Beyond technical skills, this book also emphasizes critical thinking in data analysis to empower you to make informed, data-driven decisions. The chapters enable you to harness the power of causal inference in R to uncover deeper insights from data. By the end of this book, you’ll be able to confidently establish causal relationships and make data-driven decisions with precision.What you will learn Get a solid understanding of the fundamental concepts and applications of causal inference Utilize R to construct and interpret causal models Apply techniques for robust causal analysis in real-world data Implement advanced causal inference methods, such as instrumental variables and propensity score matching Develop the ability to apply graphical models for causal analysis Identify and address common challenges and pitfalls in controlled experiments for effective causal analysis Become proficient in the practical application of doubly robust estimation using R Who this book is for This book is for data practitioners, statisticians, and researchers keen on enhancing their skills in causal inference using R, as well as individuals who aspire to make data-driven decisions in complex scenarios. It serves as a valuable resource for both beginners and experienced professionals in data analysis, public policy, economics, and social sciences. Academics and students looking to deepen their understanding of causal models and their practical implementation will also find it highly beneficial.
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization
