Complex Data Modeling and Computationally Intensive Statistical Methods

Corpus linguistics continues to be a vibrant methodology applied across highly diverse fields of research in the language sciences. With the current steep rise in corpus sizes, computational power, statistical literacy and multi-purpose software tools, and inspired by neighbouring disciplines, approaches have diversified to an extent that calls for an intensification of the accompanying critical debate. Bringing together a team of leading experts, this book follows a unique design, comparing advanced methods and approaches current in corpus linguistics, to stimulate reflective evaluation and discussion. Each chapter explores the strengths and weaknesses of different datasets and techniques, presenting a case study and allowing readers to gauge methodological options in practice. Contributions also provide suggestions for further reading, and data and analysis scripts are included in an online appendix. This is an important and timely volume, and will be essential reading for any lingui

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

This book gives an up-to-date overview of methodological and data-analytical issues of cross-cultural studies. Written by leading experts in the field, it presents the most important tools for doing cross-cultural research and outlines design considerations, methods, and analytical techniques that can improve ecological validity and help researchers to avoid pitfalls in cross-cultural psychology. By focusing on the relevant research questions that can be tackled with particular methods, it provides practical guidance on how to translate conceptual questions into decisions on study design and statistical techniques. Featuring examples from cognitive and educational assessment, personality, health, and intercultural communication and management, and illustrating key techniques in feature boxes, this concise and accessible guide is essential reading for researchers, graduate students, and professionals who work with culture-comparative data.

This book gives an up-to-date overview of methodological and data-analytical issues of cross-cultural studies. Written by leading experts in the field, it presents the most important tools for doing cross-cultural research and outlines design considerations, methods, and analytical techniques that can improve ecological validity and help researchers to avoid pitfalls in cross-cultural psychology. By focusing on the relevant research questions that can be tackled with particular methods, it provides practical guidance on how to translate conceptual questions into decisions on study design and statistical techniques. Featuring examples from cognitive and educational assessment, personality, health, and intercultural communication and management, and illustrating key techniques in feature boxes, this concise and accessible guide is essential reading for researchers, graduate students, and professionals who work with culture-comparative data.

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable

Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and append

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods,

A comprehensive text that reviews the methods and technologies that explore emergent behavior in complex systems engineering in multidisciplinary fields In Emergent Behavior in Complex Systems Enginee

Statistical Methods for Field and Laboratory Studies in Behavioral Ecology focuses on how statistical methods may be used to make sense of behavioral ecology and other data. It presents fundamental co

A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise.

This book presents an introduction to Evolutionary Game Theory (EGT) which is an emerging field in the area of complex systems attracting the attention of researchers from disparate scientific communi

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level

This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods,

This course-tested primer provides graduate students and non-specialists with a basic understanding of the concepts and methods of statistical physics and demonstrates their wide range of applications

Following the recent financial crisis, risk management in financial institutions, particularly in banks, has attracted widespread attention and discussion. Novel modeling approaches and courses to edu

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

This unique text presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and

This book provides an overview of statistical methods for measuring and monitoring healthcare performance. It covers the purpose of monitoring, data sources, types of measures for quality and safety o

The special nature of discrete variables and frequency data vis-a-vis statistical graphics is now more widely accepted, and many of these methods (e.g., mosaic displays, fourfold plots, diagnostic plo

In medical and health care the scientific method is little used, and statistical software programs are experienced as black box programs producing lots of p-values, but little answers to scientific qu

This volume presents an eclectic mix of original research articles in areas covering the analysis of ordered data, stochastic modeling and biostatistics. These areas were featured in a conference held

Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret

This widely praised book shows how equivalent statistical methods can be applied in either SAS or R, enabling users of each software package to learn how to apply the methods in the other. The book co

Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncer

Statistical methods in safety signal detection and data mining play an increasingly important role in the identification, investigation and further confirmation of safety problems that might be attrib

The monograph deals with the state of the art of current advances and developments in reduced order methods for modeling and computational reduction of complex parametrized systems, held by ordinary a

Description of a method about modeling, design and calculation of flying qualities of airplanes and Drones, with topics about simulation methods by MATLAB/SIMULINK,calculation or measurement of partic

Written in simple language with relevant examples, this illustrative introductory book presents best practices in experimental design and simple data analysis. Taking a practical and intuitive approac

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new comput

?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 statistic

This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approac

The definitive introduction to data analysis in quantitative proteomics.This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistica

The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily recor

Statistical Methods in Customer Relationship Management focuses on the quantitative and modeling aspects of customer management strategies that lead to future firm profitability, with emphasis on deve