• SLOM

  • Referenced in 6 articles [sw36331]
  • SLOM: a new measure for local spatial outliers. We propose a measure, spatial local outlier ... able to discern local spatial outliers that are usually missed by global techniques, like “three ... data point and suppresses the reporting of outliers in highly unstable areas, where data ... heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM...
  • FMS

  • Referenced in 8 articles [sw31728]
  • space, and a possibly large portion of outliers that do not lie nearby this subspace ... theorem holds for any fixed fraction of outliers (less than 1) and any fixed positive...
  • SOREX

  • Referenced in 5 articles [sw10972]
  • SOREX: subspace outlier ranking exploration toolkit. Outlier mining is an important data analysis task ... distinguish exceptional outliers from regular objects. In recent research novel outlier ranking methods propose ... focus on outliers hidden in subspace projections of the data. However, focusing only ... detection of outliers these approaches miss to provide reasons why an object should be considered...
  • MacSpin

  • Referenced in 11 articles [sw14078]
  • data as well as highly unusual observations (outliers). The program offers a broad range...
  • HDDM

  • Referenced in 11 articles [sw16420]
  • full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports...
  • mvoutlier

  • Referenced in 7 articles [sw11289]
  • package mvoutlier: Multivariate outlier detection based on robust methods. various methods for multivariate outlier detection...
  • ContaminatedMixt

  • Referenced in 10 articles [sw21014]
  • also allows for automatic detection of mild outliers via the maximum a posteriori probabilities procedure...
  • PROGRESS

  • Referenced in 10 articles [sw26316]
  • regression method is highly robust to outliers in the data. It can be computed...
  • FUNTA

  • Referenced in 5 articles [sw23476]
  • based multivariate functional pseudo-depth for shape outlier detection, JMVA ... measure especially designed for detecting shape outliers in functional data is presented. It is based ... ensured through the centring. Assuming that shape outliers in functional data follow a different pattern ... compare its performance with respect to outlier detection in simulation studies and a real data...
  • dd_tools

  • Referenced in 9 articles [sw14334]
  • toolbox provides methods for generating artificial outliers, estimating the different errors the classifiers make (false...
  • cftool

  • Referenced in 9 articles [sw14885]
  • statistics, display confidence intervals and residuals, remove outliers and assess fits with validation data. Automatically...
  • dbscan

  • Referenced in 8 articles [sw16291]
  • structure) clustering algorithms and the LOF (local outlier factor) algorithm. The implementations uses...
  • KeyGraph

  • Referenced in 8 articles [sw24377]
  • groups on undirected graph, where hubs and outliers were also located. As results, KCD could...
  • GlobalAncova

  • Referenced in 7 articles [sw30019]
  • gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares...
  • Go-ICP

  • Referenced in 6 articles [sw14979]
  • also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed...
  • robustlmm

  • Referenced in 4 articles [sw23530]
  • linear mixed-effects models often contain outliers or other contamination. Even little contamination can drive ... assess the model fit, how to detect outliers, and how to compare different fits...
  • dlookr

  • Referenced in 3 articles [sw28000]
  • information and visualization of missing values and outliers and unique and negative values to help ... statistics of univariate variables, normality tests and outliers, correlation of two variables, and relationship between ... categorizing continuous variables, imputates missing values and outliers, resolving skewness. And it creates automated reports...
  • HOS-Miner

  • Referenced in 3 articles [sw30862]
  • identify a new and interesting high-dimensional outlier detection problem in this paper, that ... subspaces in which given data points are outliers. We call the subspaces in which ... data point is an outlier as its Outlying Subspaces. In this paper, we will propose...
  • PROC CALIS

  • Referenced in 5 articles [sw12071]
  • with nonstochastic exogenous variables. You should remove outliers and consider transformations of nonnormal variables before...
  • nparLD

  • Referenced in 5 articles [sw22736]
  • methods are also robust with respect to outliers and for small sample sizes...