SB-robust estimation of mean direction for some new circular distributions

Laha, Arnab Kumar; Raja, A. C. Pravida; Mahesh, K. C.

doi:10.1007/s00362-016-0853-9

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Robust estimators have been proposed for the parameters of the vM and wrapped normal distributions (Agostinelli 2007), the vMF distribution (Kato and Eguchi 2016), and a range of other circular distributions in a series of papers referred to by Laha et al (2019).…”

Section: General Approaches To Inferencementioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

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Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere, and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper, we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (Wiley 1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, space situational awareness, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. AnWe are most grateful to three anonymous referees and, in alphabetical order, to

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Section: General Approaches To Inferencementioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

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“…Robust estimators have been proposed for the parameters of the vM and WN distributions (Agostinelli, 2007), the vMF distribution (Kato and Eguchi, 2016), and a range of other circular distributions in a series of papers referred to by Laha et al (2019).…”

Section: General Approaches To Inferencementioning

confidence: 99%

Recent advances in directional statistics

Pewsey¹,

García‐Portugués²

2020

Preprint

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Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial, and spatio-temporal data. We end with an overview of presently available software for analyzing directional data.

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Directional Data Analysis

Mahesh

2018

Advances in Analytics and Applications

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SB-robust estimation of mean direction for some new circular distributions

Cited by 3 publications

References 28 publications

Recent advances in directional statistics

Recent advances in directional statistics

Recent advances in directional statistics

Directional Data Analysis

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