Applied Multivariate Statistical Analysis: third edition

By Wolfgang Karl Härdle, Léopold Simar. 2006

537 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ffa17c6faac6cb47e40610efb80eb8ab99fe0c60

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Outlier identification in high dimensions (2008, background): “If X follows a multivariate normal distribution, the squared classic Mahalanobis distance (based upon the sample mean and covariance matrix) follows a χ2p distribution (e.g. Johnson and Wichern, 1998).”
Nitrogen-fixing bacteria communities occurring in soils under different uses in the Western Amazon Region as indicated by nodulation of siratro (Macroptilium atropurpureum) (2009, methodology): “OVA); simultaneous assessment of the relationship among the variables by canonic variable analysis (CV) and analysis of canonic correlation considering the group formed by the soil chemical and physical variables (pH, N, S, P, P-rem, K, Ca, Mg, Al, H+Al, SB, t, T, V, m, MO, Zn, Fe, Mn, Cu, B, sand a”
Estimating Number of Factors by Adjusted Eigenvalues Thresholding (2019, background): “Conditions C1-C2-C3 have been frequently imposed (Bai and Ng, 2002; Johnson and Wichern, 2007).”
On Testing the Equality of the Multiple Sharpe Ratios, with Application on the Evaluation of Ishares (2008, methodology): “The standard multivariate method known as the repeated Measures Design for comparing Treatments (see for example, Johnson and Wichern 2002, p278) is used to test H0.”
A Bayesian Reliability Approach to Multiple Response Optimization with Seemingly Unrelated Regression Models (2009, background): “, Johnson and Wichern [19]) having the same covariate structure across response types.”
Algorithms for Projection-Pursuit Robust Principal Component Analysis (2007, background): “When the projection index is the usual standard deviation, then it is known that a 1 is the eigenvector of the sample covariance matrix of the data corresponding to the largest eigenvalue (see e.g. [8]).”
Bioassessment of streams with macroinvertebrates: effect of sampled habitat and taxonomic resolution (2007, background): “Three PCs had eigenvalues .1, indicating that each accounted for .1/13 of the total variance in the 13 environmental variables (Johnson and Wichern 1988, p. 358).”