Practical Data Processing and Analysis Using R

This book presents an innovative new approach to studying source mechanisms of earthquakes, combining theory and observation in a unified methodology, with a key focus on the mechanics governing fault failures. It explains source mechanisms by building from fundamental concepts such as the equations of elasticity theory to more advanced problems including dislocation theory, kinematic models and fracture dynamics. The theory is presented first in student-friendly form using consistent notation throughout, and with full, detailed mathematical derivations that enable students to follow each step. Later chapters explain the widely-used practical modelling methods for source mechanism determination, linking clearly to the theoretical foundations, and highlighting the processing of digital seismological data. Providing a unique balance between application techniques and theory, this is an ideal guide for graduate students and researchers in seismology, tectonophysics, geodynamics and geomec

Providing an easy explanation of the fundamentals, methods, and applications of chemometrics‧ Acts as a practical guide to multivariate data analysis techniques‧ Explains the methods used in Chemometr

"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural netwo

This collection of peer-reviewed conference papers provides comprehensive coverage of cutting-edge research in topological approaches to data analysis and visualization. It encompasses the full range

The new edition of the highly praised book, Using the Biological Literature: A Practical Guide, is an annotated guide to selected resources in the biological sciences. It covers both print and electro

The only up-to-date book on this important technology, Extrusion Processing Technology: Food and Non-Food Biomaterials bridges the gap between the principles of extrusion science and the practical "

Flow Analysis: A Practical Guide reviews flow techniques for automating chemical analysis with the goal of increasing efficiency and producing better analytical results. Various applications for flow

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly ap

Computer scientists and electrical engineers present an advanced graduate textbook on the main methods, tools, and techniques for microarray image and data analysis. The topics include gridding method

In this book the practical dimension of social justice is explained using the analysis and discussion of a variety of well-known topics. These include: the relation between theory and practice in norm

A Comprehensive Source for Taking on the Next Stage of OLED R&DOLED Fundamentals: Materials, Devices, and Processing of Organic Light-Emitting Diodes brings together key topics across the fiel

In this exhaustive analysis, authors deal with the epidemiology, pathogenesis, pathoanatomy, diagnosis, and treatment of chronic radiation syndrome, using data from a unique sample of villagers in the

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Risk has always been central to finance, and managing risk depends critically on information. As evidenced by recent events, the need has never been greater for skills, systems and methodologies to manage risk information in financial markets. Authored by leading figures in risk management and analysis, this handbook serves as a unique and comprehensive reference for the technical, operational, regulatory and political issues in collecting, measuring and managing financial data. It is targeted towards a wide range of audiences, from financial industry practitioners and regulators responsible for implementing risk management systems, to system integrators and software firms helping to improve such systems. Volume 2 describes a structural and operational framework for managing a financial risk data repository. As experience accumulates on managing modern risk systems, the knowledge base of practical lessons grows. Understanding these issues and leading practices may mean the difference b

Emphasizing fundamental concepts that allow a student, novice, or practicing engineer to carry out practical design decisions, Polymer Processing: Principles and Design provides the numerical methods

This book provides a survey on wide-spread of employing wavelets analysis in different applications of speech processing. The author examines development and research in different applications of spee

Recently, the pressure for fast processing and efficient storage of large data with complex relations increased beyond the capability of traditional databases. Typical examples include iPhone applicat

How can you analyse narratives, interviews, field notes, or focus group data?Qualitative text analysis is ideal for these types of data and this textbook provides a hands-on introduction to the method

How can you analyse narratives, interviews, field notes, or focus group data?Qualitative text analysis is ideal for these types of data and this textbook provides a hands-on introduction to the method

Uniquely amongst the numerous publications to appear on the work of the French social theorist Pierre Bourdieu, this book deals with data analysis, examining a range of techniques and instruments. Aft

Building an up-to-date understanding of the methodologies that can be used to shape public health policies, Epidemiology: Study Design and Data Analysis, Second Edition encompasses the study of epidem

The up-to-date, practical guide for helping your child deal with anxiety Fear, worry, stomach pains, self-doubt—these are classic symptoms of anxiety in children. Using kid-friendly concepts and r

Through use of practical examples and a plainspoken narrative style that minimizes the use of math, this book demystifies data concepts, sources, and methods for public service professionals intereste

Large-scale data analysis ("Big Data") is suddenly of crucial importance to virtually every enterprise. Mobile and social technologies are generating massive datasets, and distributed cloud computing

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and

This volume tackles the conflicting requirements of data compression and indexing in massive datasets, by using optimization techniques to improve compression and reconfiguring data structure to incre

Substantially updated, this self-contained second edition uses Bayesian hierarchical models in the analysis of spatial data. Three new chapters cover hierarchical models for point-process data with a

Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and sh

Written for computer programming students, hobbyists, and professionals, FUNDAMENTALS OF PYTHON: DATA STRUCTURES is an introduction to object-oriented design and data structures using the popular Pyth

"This book discusses the exponential growth of information size and the innovative methods for data capture, storage, sharing, and analysis for big data"--

Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, produc

This book addresses the need to improve TCP’s performance inside data centers by providing solutions that are both practical and backward compatible with standard TCP versions. The authors approach th

Explains how to use data analysis and data driven marketing to better understand a customer base, develop smarter marketing strategies, and accurately measure the impact of marketing campaigns.

The second edition of Extrusion is designed to aid operators, engineers, and managers in extrusion processing in quickly answering practical day-to-day questions. The first part of the book provides t

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the

Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits and

How do scholars transform qualitative data into analysis? What does making analysis imply? What happens in the space in-between data and finalized analysis is notoriously difficult to talk about. In o

Variational methods have proven to be powerful techniques for solving many image processing tasks. Balancing theory with practical approaches, this book covers a wide range of variational methods and

Rcpp is the glue that binds the power and versatility of R with the speed and efficiency of C++. With Rcpp, the transfer of data between R and C++ is nearly seamless, and high-performance statistical