Robust heart rate variability analysis by generalized entropy minimization

Vecchia, Davide La; Camponovo, Lorenzo; Ferrari, Davide

doi:10.1016/j.csda.2014.09.001

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…We focus on 15 slices of the brain, so J = 64 × 64 × 15 and T = 180. To implement our robust sieve M-estimator, we need to select the tuning constant c. To this end, we propose to adapt the data-driven approach of La Vecchia et al [20] (developed in the univariate time series setting) to our high-dimensional context. Essentially, the method relies on the empirical stability of the estimates: it ensures that the selected tuning constant is such that the resulting estimating function coincides with the classical estimating function, in absence of contamination, whereas, in the presence of contamination, it yields the bounded estimating function closest to the non-robust (unbounded) one.…”

Section: Real Data Exercisementioning

confidence: 99%

Robust sieve M-estimation with an application to dimensionality reduction

Bodelet

Vecchia

2022

Electron. J. Statist.

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We propose a sieve M-estimation procedure which combines the flexibility of semiparametric inference with the stability and reliability of infinitesimal robustness. We derive the asymptotic theory of the proposed estimators, studying their convergence rate. In the context of functional magnetic resonance imaging (fMRI) data analysis, we illustrate how to apply our procedure to conduct inference on a semiparametric dynamic factor model. Monte Carlo simulations and real data analysis exemplify the stability of our estimators, providing a comparison with the extant, non robust and routinely applied sieve M-estimators.

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Section: Real Data Exercisementioning

confidence: 99%

Robust sieve M-estimation with an application to dimensionality reduction

Bodelet

Vecchia

2022

Electron. J. Statist.

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“…Smaller values of q increase robustness at the expense of reduced efficiency. La Vecchia et al (2015) suggest to select the tuning constant closest to 1 (i.e., closest to MLE) such that the estimates of the parameters are sufficiently stable, ensuring full efficiency in the absence of contamination. This is the approach we follow here, but we standardize the estimates by ' ?…”

Section: Selection Of the Tuning Constant Qmentioning

confidence: 99%

Robust estimation in beta regression via maximum Lq-likelihood

A.¹,

Ferrari²

2020

Preprint

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Beta regression models are widely used for modeling continuous data limited to the unit interval, such as proportions, fractions, and rates. The inference for the parameters of beta regression models is commonly based on maximum likelihood estimation. However, it is known to be sensitive to discrepant observations. In some cases, one atypical data point can lead to severe bias and erroneous conclusions about the features of interest. In this work, we develop a robust estimation procedure for beta regression models based on the maximization of a reparameterized L q -likelihood. The new estimator offers a trade-off between robustness and efficiency through a tuning constant. To select the optimal value of the tuning constant, we propose a data-driven method which ensures full efficiency in the absence of outliers. We also improve on an alternative robust estimator by applying our data-driven method to select its optimum tuning constant. Monte Carlo simulations suggest marked robustness of the two robust estimators with little loss of efficiency. Applications to three datasets are presented and discussed. As a by-product of the proposed methodology, residual diagnostic plots based on robust fits highlight outliers that would be masked under maximum likelihood estimation.

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