General

In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with...

Reviews withdrawn draft guidance on analytical similarity assessment.Evaluates various methods for analytical similarity evaluation based on FDA’s current guidelines.Provides a general approach for the use of n-of-1 trial design for assessment of...

This book provides a thorough understanding of distribution theory and data analysis using statistical software to solve problems related to basic statistics, probability models, and simulation. It presents a detailed explanation of different...

Innovation Diffusion Models Understand innovation diffusion models and their role in business success Innovation diffusion models are statistical models that predict the medium- and long-term sales performance of new products on a market. They account...

· Success factors and tools for all project phases· Includes application examples for various subject areas· Introduces many aspects of Data Science, from requirements analysis to data acquisition and visualizationBook InformationISBN 9781032505268Author...

Analyze Repeated Measures Studies Using Bayesian Techniques Going beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian...

This book covers random signals and random processes along with estimation of probability density function, estimation of energy spectral density and power spectral density. The properties of random processes and signal modelling are discussed with basic...

At the interface between chemistry, biology, and physics, fullerenes were one of the first objects to be dissected, scanned, and studied by the modern multi-specialty biotech community and are currently thriving in both research and practical application...

Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in...

Until recently, acquiring a background in the basic methodological principles that apply to most types of investigations meant struggling to obtain results through laborious calculations. The advent of statistical software packages has removed much of...

Learn how to read, understand, and write better SAS programs Understand the key differences between similar SAS syntax and programming approaches Save time in writing SAS code with organized summaries of important facts Improve your trouble-shooting...

Subjective Well-Being and Social Media shows how, by exploiting the unprecedented amount of information provided by the social networking sites, it is possible to build new composite indicators of subjective well-being. These new social media indicators...

Provides well-organized coverage of statistical analysis and applications in biology, kinesiology, and physical anthropology with comprehensive insights into the techniques and interpretations of R, SPSS (R), Excel (R), and Numbers (R) output An...

Features an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference Illuminating Statistical Analysis Using Scenarios...

Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in...

Although power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years, it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf)...

Novel Techniques for Analyzing and Combining Data from Modern Biological Studies Broadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies...

For surveys involving sensitive questions, randomized response techniques (RRTs) and other indirect questions are helpful in obtaining survey responses while maintaining the privacy of the respondents. Written by one of the leading experts in the world...

Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used...

Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R, Second Edition, presents methods and R code for producing high-quality static graphics, interactive visualizations, and animations of...

Now ubiquitous in modern life, spatial data present great opportunities to transform many of the processes on which we base our everyday lives. However, not only do these data depend on the scale of measurement, but also handling these data (e.g., to...

A single source of information for researchers and professionals, Traffic Simulation and Data: Validation Methods and Applications offers a complete overview of traffic data collection, state estimation, calibration and validation for traffic modelling...

This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic...

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model...

The proliferation of financial derivatives over the past decades, options in particular, has underscored the increasing importance of derivative pricing literacy among students, researchers, and practitioners. Derivative Pricing: A Problem-Based Primer...

A visual approach to data mining. Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed...

How do you draw a map of 100,000 places, of more than a million flows of people, of changes over time and space, of different kinds of spaces, surfaces and volumes, from human travel time to landscapes of hopes, fears, migration, manufacturing and...

Provides an introduction to basic structures of probability with a view towards applications in information technology A First Course in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main...

This book provides a comprehensive and up to date treatment of theory and practical implementation in Register-based statistics. It begins by defining the area, before explaining how to structure such systems, as well as detailing alternative approaches...

Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first...

Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry,...

A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical level Written in an engaging and entertaining manner, the revised and updated second edition of Probably Not continues to offer an...

Provides a simple exposition of the basic time series material, and insights into underlying technical aspects and methods of proof Long memory time series are characterized by a strong dependence between distant events. This book introduces readers...

An essential guide to two burgeoning topics in machine learning - classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a...

A hands-on approach to statistical inference that addresses the latest developments in this ever-growing field This clear and accessible book for beginning graduate students offers a practical and detailed approach to the field of statistical inference,...

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance...

A comprehensive treatment of systems and software testing using state of the art methods and tools This book provides valuable insights into state of the art software testing methods and explains, with examples, the statistical and analytic methods used...

A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for railway track engineering, Big Data and Differential...

Set includes Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel ISBN 978-1-119-22097-8 and the accompanying Solutions Manual ISBN 978-1-119-22102-9 A well-balanced and accessible introduction to the...

Presents information sources and methodologies for modeling and simulating banking system stability Combining both academic and institutional knowledge and experience, Banking Systems Simulation: Theory, Practice, and Application of Modeling Shocks,...