Journal ArticleDOI
Imputation of missing values for compositional data using classical and robust methods
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TLDR
The results show that the proposed methods outperform standard imputation methods in the presence of outliers, and the model-based method with robust regressions is preferable.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2010-12-01. It has received 218 citations till now. The article focuses on the topics: Imputation (statistics) & Missing data.read more
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Book
Flexible Imputation of Missing Data
TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
Book ChapterDOI
robCompositions: An R-package for Robust Statistical Analysis of Compositional Data
TL;DR: The R-package robCompositions (Templ et al., 2009) contains functions for robust statistical methods designed for compositional data, like principal component analysis, factor analysis, and discriminant analysis.
Journal ArticleDOI
Missing value imputation: a review and analysis of the literature (2006–2017)
TL;DR: This paper aims at reviewing and analyzing related studies carried out in recent decades, from the experimental design perspective, and identifying limitations in the existing body of literature based upon which some directions for future research can be gleaned.
Journal ArticleDOI
Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values
TL;DR: In this article, an analysis of logratio-and ln-transformed stream sediment geochemical data containing ca. 30% of samples with censored values of a pathfinder element for the mineral deposit-type of interest yielded the following findings: Exclusion of those samples supports interpretation of multi-element anomalies reflecting the presence of mineralization.
Journal ArticleDOI
The concept of compositional data analysis in practice--total major element concentrations in agricultural and grazing land soils of Europe.
Clemens Reimann,Peter Filzmoser,Karl Fabian,Karel Hron,Manfred Birke,Alecos Demetriades,Enrico Dinelli,Anna Ladenberger +7 more
TL;DR: Because the difference between the two methods should be most pronounced in large-scale, and therefore highly variable, datasets, here a new dataset of agricultural soils, covering all of Europe, is used to demonstrate and compare both approaches.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Journal ArticleDOI
Statistical Analysis with Missing Data
Book
Analysis of Incomplete Multivariate Data
TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.