Estimation effects on powers of two simple test statistics in identifying an outlier in linear models

Sutradhar, Brajendra C.; Chu, David P.; Bari, Wasimul

doi:10.1080/10629360600609901

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…There exists a vast literature for robust inference in linear models in the presence of one or more outliers; see, for example, Rousseeuw & Leroy (1987), Huber (2004), and Sutradhar et al. (2007).…”

Section: Introductionmentioning

confidence: 99%

On Bias Reduction in Robust Inference for Generalized Linear Models

Bari

Sutradhar

2010

Scandinavian Journal of Statistics

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It is well known that one or more outlying points in the data may adversely affect the consistency of the quasi-likelihood or the likelihood estimators for the regression effects. Similar to the quasi-likelihood approach, the existing outliers-resistant Mallow's type quasi-likelihood (MQL) estimation approach may also produce biased regression estimators. As a remedy, by using a fully standardized score function in the MQL estimating equation, in this paper, we demonstrate that the fully standardized MQL estimators are almost unbiased ensuring its higher consistency performance. Both count and binary responses subject to one or more outliers are used in the study. The small sample as well as asymptotic results for the competitive estimators are discussed. Copyright (c) 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

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Section: Introductionmentioning

confidence: 99%

On Bias Reduction in Robust Inference for Generalized Linear Models

Bari

Sutradhar

2010

Scandinavian Journal of Statistics

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“…Since we are using a frequency domain DOT system, each data set consists of amplitude and phase data. The maximum normed residual (MNR) is a widely used statistical test to address the problem of outlier rejection [25] that has shown outstanding performance in both linear and nonlinear data [26]. The MNR test is based on the largest absolute deviation from the sample mean in units of the sample standard deviation.…”

Section: Outlier Rejection In Reference Measurementsmentioning

confidence: 99%

Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging

Vavadi

Zhu

2016

Biomed. Opt. Express

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Imaging-guided near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, preprocessing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%. Czerniecki, "Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study," Acad. Radiol. 12(8), 925-933 (2005). 9.

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Robust Inference Progress from Independent to Longitudinal Setup

Sutradhar

2013

ISS-2012 Proceedings Volume on Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers

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Estimation effects on powers of two simple test statistics in identifying an outlier in linear models

Cited by 4 publications

References 19 publications

On Bias Reduction in Robust Inference for Generalized Linear Models

On Bias Reduction in Robust Inference for Generalized Linear Models

Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging

Robust Inference Progress from Independent to Longitudinal Setup

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