A2 Refereed review article in a scientific journal
Robust Nonparametric Inference
Authors: Nordhausen K, Oja H
Publisher: ANNUAL REVIEWS
Publication year: 2018
Journal: Annual Review of Statistics and Its Application
Journal name in source: ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 5
Journal acronym: ANNU REV STAT APPL
Volume: 5
First page : 473
Last page: 500
Number of pages: 28
ISSN: 2326-8298
eISSN: 2326-831X
DOI: https://doi.org/10.1146/annurev-statistics-031017-100247
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/31111091
Abstract
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.
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.
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