Robust Transformation of Proportions Using the Forward Search

Bini, Matilde; Bertaccini, Bruno

doi:10.1007/3-540-35978-8_20

Cited by 1 publication

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“…It is recommended to transform very non-gaussian variables to approximate gaussianity, like we took the logarithm of the right-skewed variable Price in the cars data in Section 3. More general tools are the Box-Cox and Yeo-Johnson (Yeo and Johnson, 2000) transformations, that have parameters which need to be estimated as done by Bini and Bertacci (2006) and Riani (2008). We chose not to automate this step because we feel it is best carried out by a person with subject matter knowledge about the data.…”

Section: Detailed Description Of the Algorithmmentioning

confidence: 99%

Detecting Deviating Data Cells

Rousseeuw

Bossche

2017

Technometrics

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A multivariate dataset consists of n cases in d dimensions, and is often stored in an n by d data matrix. It is well-known that real data may contain outliers. Depending on the situation, outliers may be (a) undesirable errors which can adversely affect the data analysis, or (b) valuable nuggets of unexpected information. In statistics and data analysis the word outlier usually refers to a row of the data matrix, and the methods to detect such outliers only work when at least half the rows are clean. But often many rows have a few contaminated cell values, which may not be visible by looking at each variable (column) separately. We propose the first method to detect deviating data cells in a multivariate sample which takes the correlations between the variables into account. It has no restriction on the number of clean rows, and can deal with high dimensions. Other advantages are that it provides predicted values of the outlying cells, while imputing missing values at the same time. We illustrate the method on several real data sets, where it uncovers more structure than found by purely columnwise methods or purely rowwise methods. The proposed method can help to diagnose why a certain row is outlying, e.g. in process control. It also serves as an initial step for estimating multivariate location and scatter matrices.

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Section: Detailed Description Of the Algorithmmentioning

confidence: 99%