Titlebook: Visualization and Imputation of Missing Values; With Applications in Matthias Templ Book 2023 The Editor(s) (if applicable) and The Author(

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Deductive Imputation and Outlier Replacement,eletion and imputation. Note that these rules—although efficient and important in many situations—are strictly deterministic and ignore the probabilistic component when working with samples from a population..The second part of this chapter deals with the replacement of outliers. After identifying p

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Imputation Without a Formal Statistical Model,omatically, as is the case with many model-based imputation methods. Imputation methods that do not rely on a statistical model are then often the preferred choice. Moreover, they are often used because of their simplicity and good general performance. In this chapter, hot-deck methods, .-nearest ne

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Nonlinear Methods,sier to interpret, and (3) for small . and/or large ., linear models are often the only way to avoid overfitting the data..What is new in this chapter is the consideration of nonlinearities between variables, that is, those that do not disappear by transforming variables or including quadratic terms

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Methods for Compositional Data,a sample, or chemical concentrations of elements in general, or time-use per day, or expenditures, wages or income for different components, or any data where the rows add up to constants. Then you should get familiar with compositional data analysis, log-ratio analysis, and corresponding imputation

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Book 2023t 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, disadvanta

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