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Simulation for Data Science with R
Harness actionable insights from your data with computational statistics and simulations using R About This Book Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulation Who This Book Is For This book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R...
Statistical Disclosure Control for Microdata
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, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased sign...
Visualization and Imputation of Missing Values
This book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R. It explains the concepts of common imputation methods with a focus on visualization, description of data problems and practical solutions using R, including modern methods of robust imputation, imputation based on deep learning and imputation for complex data. By describing the advantages, disadvantages and pitfalls of each method, the book presents a clear picture of which imputation methods are applicable given a specific data set at hand. The material covered includes the pre-analysis of data, visualization of missing values in incomplete data...
Applied Compositional Data Analysis
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
New Developments in Statistical Disclosure Controland Imputation
The aim of statistical disclosure control is to keep up the required statistical privacy while making data available to the researchers. This can be achieved with the help of minimal modifications of the data without changing the multivariate data structure. In this book the well-developed R package sdc- Micro is introduced. With the help of this package it is possible to keep microdata confidential in a very effective way. The concept is thoroughly explained and its application is demonstrated using real-world data. In addition to that, the robustification of disclosure methods is described. Many SDCmethods for microdata developed so far can be influenced by outliers to a great extent resulting in a high loss of information of the perturbed data. Missing values are the second topic of this book. The application of visualisation tools for the analysis of missing values, preceding the choice of an imputation method, is highlighted. In addition to that, new methods for the imputation of composition data are introduced. Due to the linear dependence of the variables from compositional data, reasonalbe imputations can be made by considering the special nature of such data.
Combining Soft Computing and Statistical Methods in Data Analysis
Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishi...
Advances in Compositional Data Analysis
This book presents modern methods and real-world applications of compositional data analysis. It covers a wide variety of topics, ranging from an updated presentation of basic concepts and ideas in compositional data analysis to recent advances in the context of complex data structures. Further, it illustrates real-world applications in numerous scientific disciplines and includes references to the latest software solutions available for compositional data analysis, thus providing a valuable and up-to-date guide for researchers and practitioners working with compositional data. Featuring selected contributions by leading experts in the field, the book is dedicated to Vera Pawlowsky-Glahn on the occasion of her 70th birthday.
Studies in Theoretical and Applied Statistics
This book includes a wide selection of papers presented at the 50th Scientific Meeting of the Italian Statistical Society (SIS2021), held virtually on 21-25 June 2021. It covers a wide variety of subjects ranging from methodological and theoretical contributions to applied works and case studies, giving an excellent overview of the interests of the Italian statisticians and their international collaborations. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.
Visualization and Imputation of Missing Values
This book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R. It explains the concepts of common imputation methods with a focus on visualization, description of data problems and practical solutions using R, including modern methods of robust imputation, imputation based on deep learning and imputation for complex data. By describing the advantages, disadvantages and pitfalls of each method, the book presents a clear picture of which imputation methods are applicable given a specific data set at hand. The material covered includes the pre-analysis of data, visualization of missing values in incomplete data...
