Robust Nonparametric Inference



: Nordhausen K, Oja H

Publisher: ANNUAL REVIEWS

: 2018

: Annual Review of Statistics and Its Application

: ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 5

: ANNU REV STAT APPL

: 5

: 473

: 500

: 28

: 2326-8298

: 2326-831X

DOI: https://doi.org/10.1146/annurev-statistics-031017-100247

: https://research.utu.fi/converis/portal/detail/Publication/31111091


In this article, we provide a personal review of the literature on nonparametric and robust tools in the standard univariate and multivariate location and scatter, as well as linear regression problems, with a special focus on sign and rank methods, their equivariance and invariance properties, and their robustness and efficiency. Beyond parametric models, the population quantities of interest are often formulated as location, scatter, skewness, kurtosis and other functionals. Some old and recent tools for model checking, dimension reduction, and subspace estimation in wide semiparametric models are discussed. We also discuss recent extensions of procedures in certain nonstandard semiparametric cases including clustered and matrix-valued data. Our personal list of important unsolved and future issues is provided.

Last updated on 2024-26-11 at 23:39