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001484458 001__ 1484458
001484458 003__ OCoLC
001484458 005__ 20240117003325.0
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001484458 019__ $$a1411278037
001484458 020__ $$a9783031300738
001484458 020__ $$a3031300734
001484458 020__ $$z3031300726
001484458 020__ $$z9783031300721
001484458 0247_ $$a10.1007/978-3-031-30073-8$$2doi
001484458 035__ $$aSP(OCoLC)1411306531
001484458 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dEBLCP
001484458 049__ $$aISEA
001484458 050_4 $$aQA76.9.I52
001484458 08204 $$a001.4/22602855133$$223/eng/20231211
001484458 1001_ $$aTempl, Matthias.
001484458 24510 $$aVisualization and imputation of missing values :$$bwith applications in R /$$cMatthias Templ.
001484458 260__ $$aCham :$$bSpringer International Publishing AG,$$c2023.
001484458 300__ $$a1 online resource (xxii, 462 pages) :$$billustrations (chiefly color).
001484458 4901_ $$aStatistics and Computing Series
001484458 504__ $$aIncludes bibliographical references and index.
001484458 5050_ $$aIntro -- Preface -- Limitations -- Mathematical Notation -- Code Excerpt at GitHub -- Acknowledgement -- Reference -- Contents -- 1 Topic-Focused Introduction to R and Data sets Used -- 1.1 Resources and Software for the Imputation of Missing Values -- 1.1.1 Amelia -- 1.1.2 mi -- 1.1.3 mice (and BaBooN) -- 1.1.4 missMDA -- 1.1.5 missForest and missRanger -- 1.1.6 robCompositions -- 1.1.7 VIM -- 1.2 The Statistics Environment R -- 1.3 Simple Calculations in R -- 1.4 Installation of and Updates -- 1.5 Help -- 1.6 The R Workspace and the Working Directory -- 1.7 Data Types
001484458 5058_ $$a1.8 Generic Functions, Methods, and Classes -- 1.9 A Note on Functions with the Same Name in Different Packages -- 1.10 Basic Data Manipulation with the dplyr Package -- 1.10.1 Pipes -- 1.10.2 dplyr-tibbles -- 1.10.3 dplyr-Selection of Rows -- 1.10.4 dplyr-Order -- 1.10.5 dplyr-Selection of Columns -- 1.10.6 dplyr-Uniqueness -- 1.10.7 dplyr-Creating Variables -- 1.10.8 dplyr-Grouping and Summary Statistics -- 1.10.9 dplyr-Window Functions -- 1.11 Data Manipulation with the data.table Package -- 1.11.1 data.table-Variable Construction -- 1.11.2 data.table-Indexing/Subsetting
001484458 5058_ $$a1.11.3 data.table-Keys -- 1.11.4 data.table-Fast Subsetting -- 1.11.5 data.table-Calculations in Groups -- 1.12 Data Sets -- 1.12.1 Census Data from UCI -- 1.12.2 Airquality -- 1.12.3 Breast Cancer -- 1.12.4 Brittleness Index -- 1.12.5 Kola C-horizon Data -- 1.12.6 Colic Horse Data -- 1.12.7 New York Collission Data -- 1.12.8 Diabetes -- 1.12.9 Austrian EU-SILC Data -- 1.12.10 Food Consumption -- 1.12.11 Pulp Lignin -- 1.12.12 Structural Business Statistics Data -- 1.12.13 Mammal Sleep Data -- 1.12.14 West Pacific Tropical Atmosphere Ocean Data -- 1.12.15 Wine Tasting and Price of Wines
001484458 5058_ $$a1.12.16 Further Data Sets -- References -- 2 Distribution, Pre-analysis of Missing Values and Data Quality -- 2.1 Introduction -- 2.2 How Does Missing Data Arise? -- 2.2.1 Surveys in Official Statistics and Surveys Obtained with Questionaires -- 2.2.2 Comment on Structural Zeros and Non-applicable Questions in a Questionaire -- 2.2.3 Missing Values from Measuring Experiments -- 2.2.4 Censored Values -- 2.2.5 Monotone Missingness -- 2.3 Missing Value Mechanisms -- 2.3.1 Missing at Random (MAR) -- 2.3.2 Missing at Completely Random (MCAR) -- 2.3.3 Missing Not at Random (MNAR) -- 2.3.4 Example
001484458 5058_ $$a2.3.5 Summary on MCAR, MAR, and MNAR -- 2.4 Limitations for the Detection of the Missing Value Mechanisms -- 2.5 Kinds of Attributes -- 2.5.1 Binary and Nominal Variables and Related Distances -- 2.5.2 Ordered Categorical Variables -- 2.5.3 Count Variables, Continuous Variables, Semi-continuous Variables, and Related Distances -- 2.5.4 The Gower Distance -- 2.6 Data Quality and Consistency of Data -- 2.6.1 Outliers -- 2.6.1.1 Outliers in Relation with Other Data Problems -- 2.6.1.2 What Is an Outlier and When an Outlier Should Be Deleted and Imputed? -- 2.6.1.3 Univariate Methods
001484458 506__ $$aAccess limited to authorized users.
001484458 520__ $$aThis book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R. It explains the concepts of common imputation methods with a focus on visualization, description of data problems and practical solutions using R, including modern methods of robust imputation, imputation based on deep learning and imputation for complex data. By describing the advantages, disadvantages and pitfalls of each method, the book presents a clear picture of which imputation methods are applicable given a specific data set at hand. The material covered includes the pre-analysis of data, visualization of missing values in incomplete data, single and multiple imputation, deductive imputation and outlier replacement, model-based methods including methods based on robust estimates, non-linear methods such as tree-based and deep learning methods, imputation of compositional data, imputation quality evaluation from visual diagnostics to precision measures, coverage rates and prediction performance and a description of different model- and design-based simulation designs for the evaluation. The book also features a topic-focused introduction to R and R code is provided in each chapter to explain the practical application of the described methodology. Addressed to researchers, practitioners and students who work with incomplete data, the book offers an introduction to the subject as well as a discussion of recent developments in the field. It is suitable for beginners to the topic and advanced readers alike.
001484458 588__ $$aDescription based upon print version of record.
001484458 650_6 $$aVisualisation de l'information$$xInformatique.
001484458 650_6 $$aR (Langage de programmation)
001484458 650_6 $$aObservations manquantes (Statistique)$$xInformatique.
001484458 650_0 $$aInformation visualization$$xData processing.$$0(DLC)sh2002000243
001484458 650_0 $$aR (Computer program language)$$0(DLC)sh2002004407
001484458 650_0 $$aMissing observations (Statistics)$$xData processing.$$0(DLC)sh 85086013
001484458 655_0 $$aElectronic books.
001484458 77608 $$iPrint version:$$aTempl, Matthias$$tVisualization and Imputation of Missing Values$$dCham : Springer International Publishing AG,c2023$$z9783031300721
001484458 830_0 $$aStatistics and computing.
001484458 852__ $$bebk
001484458 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-30073-8$$zOnline Access$$91397441.1
001484458 909CO $$ooai:library.usi.edu:1484458$$pGLOBAL_SET
001484458 980__ $$aBIB
001484458 980__ $$aEBOOK
001484458 982__ $$aEbook
001484458 983__ $$aOnline
001484458 994__ $$a92$$bISE