A Simpler, Affine-Invariant, Multivariate, Distribution-Free Sign Test

By Ronald H. Randles. 2000

123 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9a770d74ccd72793cef5da2e56a113fd425f7ecf

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In Statistics and Probability

Citations in the corpus

Multivariate Nonparametric Tests (2004, unknown): “This test was developed by Randles (2000).”
A canonical definition of shape (2008, unknown): “Classical choices include (i) S(Σ) = Σ11 (Hallin and Paindaveine (2006a), Hallin et al. (2006), Hettmansperger and Randles (2002), and Randles (2000)), (ii) S(Σ) = (trΣ)/k (Dümbgen (1998), Ollila et al. (2004), and Tyler (1987)), and (iii) S(Σ) = |Σ|1/k (Dümbgen and Tyler (2005), Hallin and…”
SEMIPARAMETRICALLY EFFICIENT RANK-BASED INFERENCE FOR SHAPE II. OPTIMAL R-ESTIMATION OF SHAPE (2006, unknown): “, [40]).”
Sign test of independence between two random vectors (2003, methodology): “The standardized spatial signs described in Section 2 are based on the approach launched in Randles (2000) and Hettmansperger and Randles (2002).”
Rank-based optimal tests of the adequacy of an elliptic VARMA model (2004, background): “See Randles (2000) for a proof.”
Optimal tests for multivariate location based on interdirections and pseudo-Mahalanobis ranks (2002, methodology): “The estimator was obtained via the iterative scheme described in Randles (2000).”
Affine-invariant aligned rank tests for the multivariate general linear model with VARMA errors (2005, background): “T ˜ (n)′ II;J(θ̂) P̂ M̂ { N̂− [ I− N̂ΥII ( Υ ′ II N̂ΥII )− Υ ′ II N̂ N̂ − ]} M̂ ′ P̂ ′ T ˜ (n) II;J(θ̂), that is, in view of Lemma 2.2.6(c) in Rao and Mitra (1971), W(n)II;f?”
Optimal procedures based on interdirections and pseudo-Mahalanobis ranks for testing multivariate elliptic white noise against ARMA dependence (2002, methodology): “The estimator was obtained via the iterative scheme described in Randles (2000).”
Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem (2016, methodology): “We can adapt the recursive algorithm of Hettmansperger and Randles (2002) to solve (3).”
A nonparametric multivariate multisample test based on data depth (2012, unknown): “…(1955), Hössjer and Croux (1995), Liu and Singh (1993), Möttönen et al. (2003), Möttönen and Oja (1995), Oja (1999), Oja and Nyblom (1989), Peters and Randles (1990), Peters and Randles (1991), Randles (1989), Randles (2000), Randles and Peters (1990), Um and Randles (1998), Zuo and He (2006).”
On the Breakdown Properties of Some Multivariate M‐Functionals * (2005, methodology): “More recently, it has been applied in the development of non- parametric multivariate methods, see e.g. Randles (2000), Hallin & Paindaveine (2002a, 2002b) and Hettmansperger & Randles (2003).”