Robust Estimation of a Location Parameter

Huber, Peter J.

doi:10.1007/978-1-4612-4380-9_35

Cited by 1,170 publications

(905 citation statements)

References 5 publications

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“…One way to address this concern is to use M-estimation (Huber, 1964), which puts less weight on residuals that are more likely to be outliers. In addition, we reestimate the model by excluding some observations that look like outliers.…”

Section: Outliers and Mean Reversionmentioning

confidence: 99%

Do Risk Preferences Change? Evidence from Panel Data before and after the Great East Japan Earthquake

Hanaoka

Shigeoka

Watanabe³

2015

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We investigate whether individuals' risk preferences change after experiencing a natural disaster, specifically, the 2011 Great East Japan Earthquake. Exploiting the panels of nationally representative surveys on risk preferences, we find that men who experienced greater intensity of the Earthquake became more risk tolerant after the Earthquake. Furthermore, these men gamble more, which is consistent with the direction of changes in risk preferences. We find no such pattern for women. Finally, the effects on men's risk preferences are persistent even five years after the Earthquake at almost the same magnitude as those shortly after the Earthquake.

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Section: Outliers and Mean Reversionmentioning

confidence: 99%

Do Risk Preferences Change? Evidence from Panel Data before and after the Great East Japan Earthquake

Hanaoka

Shigeoka

Watanabe³

2015

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“…A sample mean is used to estimate the difference of expression intensity levels for the paired t-test, which is highly sensitive when there are extreme values or outliers, particularly when the sample size is small. More robust statistics, such as median and Huber M-estimator [22,23], would be less sensitive to such extreme values. These different options can be chosen by each individual using our software program.…”

Section: Discussionmentioning

confidence: 99%

Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry

et al. 2007

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LC-MS/MS with certain labeling techniques such as isotope-coded affinity tag (ICAT) enables quantitative analysis of paired protein samples. However, current identification and quantification of differentially expressed peptides (and proteins) are not reliable for large proteomics screening of complex biological samples. The number of replicates is often limited because of the high cost of experiments and the limited supply of samples. Traditionally, a simple fold change cutoff is used, which results in a high rate of false positives. Standard statistical methods such as the two-sample ttest are unreliable and severely underpowered due to high variability in LC-MS/MS data, especially when only a small number of replicates are available. Using an advanced error pooling technique, we propose a novel statistical method that can reliably identify differentially expressed proteins while maintaining a high sensitivity, particularly with a small number of replicates. The proposed method was applied both to an extensive simulation study and a proteomics comparison between microparticles (MPs) generated from platelet (platelet MPs) and MPs isolated from plasma (plasma MPs). In these studies, we show a significant improvement of our statistical analysis in the identification of proteins that are differentially expressed but not detected by other statistical methods. In particular, several important proteins -two peptides for b-globin and three peptides for von Willebrand Factor (vWF) -were identified with very small false discovery rates (FDRs) by our method, while none was significant when other conventional methods were used. These proteins have been reported with their important roles in microparticles in human blood cells: vWF is a platelet and endothelial cell product that binds to P-selectin, GP1b, and GP IIb/IIIa, and b-globin is one of the peptides of hemoglobin involved in the transportation of oxygen by red blood cells.

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“…(7) and (9) using a robust nonlinear least-squares method implemented in the Solverstat [24] Excel Ò macro. Because of the natural noise affecting this kind of data an iteratively reweighted least-squares regression using the Huber robust weight function has been adopted [25]. For each run, moreover, a (robust) regression diagnostic to detect the possible outlier has been applied.…”

Section: Robust Treatment Of the Spectrophotometric Deconvoluted Datamentioning

confidence: 99%

Deconvolution procedure of the UV–vis spectra. A powerful tool for the estimation of the binding of a model drug to specific solubilisation loci of bio-compatible aqueous surfactant-forming micelle

Calabrese

Merli

Liveri

2015

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Robust Estimation of a Location Parameter

Cited by 1,170 publications

References 5 publications

Do Risk Preferences Change? Evidence from Panel Data before and after the Great East Japan Earthquake

Do Risk Preferences Change? Evidence from Panel Data before and after the Great East Japan Earthquake

Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry

Deconvolution procedure of the UV–vis spectra. A powerful tool for the estimation of the binding of a model drug to specific solubilisation loci of bio-compatible aqueous surfactant-forming micelle

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