On Robust estimation of a correlation coefficient

Shevlyakov, Georgy

doi:10.1007/bf02400929

Cited by 53 publications

(54 citation statements)

References 4 publications

(12 reference statements)

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“…Since then it has shown good results both as a standalone scale estimator and also as an auxiliary step in more complex tasks such as the robust estimation of a correlation coefficient with robust dispersions (Shevlyakov and Smirnov, 2011), detection of outliers by robust boxplots (Shevlyakov et al, 2013a), and the robust estimation of power spectra (Shevlyakov et al, 2013b). The last problem is particularly sensitive not only to the efficiency of an estimator in use but also to its computation time.…”

Section: Discussionmentioning

confidence: 98%

Fast highly efficient and robust one-step -estimators of scale based on

Smirnov

Shevlyakov

2014

Computational Statistics & Data Analysis

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Section: Discussionmentioning

confidence: 98%

Fast highly efficient and robust one-step -estimators of scale based on

Smirnov

Shevlyakov

2014

Computational Statistics & Data Analysis

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“…Much higher efficiency 0.81 with the same breakdown point 0.5 can be provided by using the F Q correlation coefficient (Shevlyakov and Smirnov, 2011) …”

Section: Preliminariesmentioning

confidence: 96%

“…Basically, to obtain good robust estimates of power spectra, we use highly efficient robust estimates of scale and correlation (Shevlyakov and Smirnov, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Few Remarks on Robust Estimation of Power Spectra

Shevlyakov

Lyubomishchenko

Smirnov

2014

AJS

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Various robust versions of the classical methods of power spectra estimation are considered. Their performance evaluation is studied in autoregressive models with contamination. It is found out that the best robust estimates of power spectra are based on robust highly efficient estimates of autocovariances. Several open problems for future research are formulated.Keywords: power spectra, robustness, autoregressive models. A Few Remarks on Robust Estimation of Power SpectraGeorgy Shevlyakov, Nickolay Lyubomishchenko and Pavel Smirnov St. Petersburg State Polytechnic University, RussiaAbstract: Various robust versions of the classical methods of power spectra estimation are considered. Their performance evaluation is studied in autoregressive models with contamination. It is found out that the best robust estimates of power spectra are based on robust highly efficient estimates of autocovariances. Several open problems for future research are formulated.

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“…Squaring of such a measure to obtain weights further aggravates its sensitivity to nonlinearity and extreme values. Therefore, other measures of correlation such as signum correlation, rank correlation, Kenall's tau, absolute correlation (Bradley, 1985), Shevlyakov's correlation (Shevlyakov, 1997), Brownian correlation (Székely and Rizzo, 2009), etc. might be considered for measuring the degree of concordance between the composite index and the indicator variables.…”

Section: Construction Of Composite Indicesmentioning

confidence: 99%