Robust Statistics: The Approach Based on Influence Functions

Robust Statistics

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

Definitions, methods, and applications in interpretable machine learning

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Less is more: meta-analyses of sensitivity and attachment interventions in early childhood

Bakermans-Kranenburg, M. J., van IJzendoorn, M. H., & Juffer, F. (2003) · Psychological Bulletin · doi:10.1037/0033-2909.129.2.195 · Cited by 1,883 · see citing works

A better lemon squeezer? maximum-likelihood regression with beta-distributed dependent variables.

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Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

Motulsky, H. J., & Brown, R. E. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-123 · Cited by 1,616 · see citing works

Statistical data analysis in the computer age

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Adversarial machine learning

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The neglect of child neglect: A meta-analytic review of the prevalence of neglect

Stoltenborgh, M., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2013) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-012-0549-y · Cited by 736 · see citing works

Sleep, cognition, and behavioral problems in school-age children: a century of research meta-analyzed.

Astill, R. G., Van der Heijden, K. B., Van IJzendoorn, M. H., & Van Someren, E. J. W. (2012) · Psychological Bulletin · doi:10.1037/a0028204 · Cited by 707 · see citing works

Reverse engineering gene networks using singular value decomposition and robust regression

Yeung, M. K. S., Tegnér, J., & Collins, J. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.092576199 · Cited by 667 · see citing works

Influence functions of the Spearman and Kendall correlation measures

Croux, C., & Dehon, C. (2010) · Statistical Methods & Applications · doi:10.1007/s10260-010-0142-z · Cited by 634 · see citing works

Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion

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Adoption and cognitive development: A meta-analytic comparison of adopted and nonadopted children's IQ and school performance

van IJzendoorn, M. H., Juffer, F., & Poelhuis, C. W. K. (2005) · Psychological Bulletin · doi:10.1037/0033-2909.131.2.301 · Cited by 488 · see citing works

Qualitative Comparative Analysis (QCA) and Related Systematic Comparative Methods: Recent Advances and Remaining Challenges for Social Science Research

Rihoux, B. (2006) · International Sociology · doi:10.1177/0268580906067836 · Cited by 461 · see citing works

Principles and procedures of exploratory data analysis.

Behrens, J. T. (1997) · Psychological Methods · doi:10.1037/1082-989x.2.2.131 · Cited by 465 · see citing works

Fit Indices Versus Test Statistics

Yuan, K.-H. (2005) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr4001_5 · Cited by 415 · see citing works

Attachment-based intervention for enhancing sensitive discipline in mothers of 1- to 3-year-old children at risk for externalizing behavior problems: a randomized controlled trial

Van Zeijl, J., Mesman, J., Van IJzendoorn, M. H., Bakermans-Kranenburg, M. J., Juffer, F., Stolk, M. N., Koot, H. M., & Alink, L. R. A. (2006) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.74.6.994 · Cited by 346 · see citing works

Robust statistics in data analysis — A review

Daszykowski, M., Kaczmarek, K., Vander Heyden, Y., & Walczak, B. (2007) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2006.06.016 · Cited by 331 · see citing works

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362 · see citing works

Theory of cortical function

Heeger, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619788114 · Cited by 220 · see citing works

Robust guided image filtering using Nonconvex potentials

Ham, B., Cho, M., & Ponce, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2669034 · Cited by 219 · see citing works

Magnetic resonance imaging evidence for presymptomatic change in thalamus and caudate in familial Alzheimer’s disease

Ryan, N. S., Keihaninejad, S., Shakespeare, T. J., Lehmann, M., Crutch, S. J., Malone, I. B., Thornton, J. S., Mancini, L., Hyare, H., Yousry, T., et al. (2013) · Brain · doi:10.1093/brain/awt065 · Cited by 205 · see citing works

Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security

Kuppa, A., & Le-Khac, N.-A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206780 · Cited by 123 · see citing works

Epigenetic changes in the CRH gene are related to severity of suicide attempt and a general psychiatric risk score in adolescents

Jokinen, J., Boström, A. E., Dadfar, A., Ciuculete, D. M., Chatzittofis, A., Åsberg, M., & Schiöth, H. B. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.018 · Cited by 67 · see citing works

Anorexia Nervosa, Autism, and the ADOS: How Appropriate Is the New Algorithm in Identifying Cases?

Sedgewick, F., Kerr-Gaffney, J., Leppanen, J., & Tchanturia, K. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00507 · Cited by 34 · see citing works