Development of robust scatter estimators under independent contamination model
|
|
- Elmer Briggs
- 5 years ago
- Views:
Transcription
1 Development of robust scatter estimators under independent contamination model C. Agostinelli 1, A. Leung, V.J. Yohai 3 and R.H. Zamar 1 Universita Cà Foscàri di Venezia, University of British Columbia, and 3 Universidad de Buenos Aires and CONICET Mar 16, 013
2 Some declarations To math geeks: I am sorry but I will keep my talk to have minimal math equations and theorems today (come on, it is 9 am!)
3 Objective of the day Objective: robust estimation of (location and) scatter matrix for a data set of size n and p continuous variables.
4 What is contamination?
5 What is contamination? Perhaps the most classical contamination model is Huber-Tukey contamination model (HTCM) (Tukey in 1960, Huber in 1964), which was originally for 1-D data... Contamination is row-wise, e.g. [,1] [,] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] [,] [3,] [4,] [5,] [6,] [7,]
6 What is contamination? HTCM in math notation, x = (1 u)x + uc where x = (x 1,..., x p ) N(µ, Σ) c something u Bin(1, ɛ), 0 ɛ < 1/
7 New contamination model HTCM may not be realistic... outliers are more likely to happen in certain variables, independent of others what if p is large but n is of moderate to small size? what if every single observation has one component contamination?
8 New contamination model HTCM may not be realistic... outliers are more likely to happen in certain variables, independent of others what if p is large but n is of moderate to small size? what if every single observation has one component contamination? Alqallaf, Van Aelst, Yohai and Zamar (006) proposed a new contamination model... Cell-wise contamination model
9 New contamination model Contamination is cell-wise, e.g. [,1] [,] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] [,] [3,] [4,] [5,] [6,]
10 New contamination model Contamination is cell-wise, e.g. [,1] [,] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] [,] [3,] [4,] [5,] [6,] where in math model is x = (1 U)x + Uc where x = (x 1,..., x p ) and c is same as before, except U = diag(u i ), where u i Bin(1, ɛ), 0 ɛ < 1/
11 Existing robust scatter estimators Under HTCM, we have... Minimum Volume Ellipsoid (MVE) (Rousseeuw, 1985) Minimum Covariance Determinant (MCD) (Rousseeuw, 1985) S-estimator (Davies, 1987) MM-estimator (Yohai, 1987; Tatsuoka and Tyler, 000) modified GK estimator (Maronna and Zamar, 00)... Let s look at how these existing robust scatter estimators (e.g. MVE, S-est, MM-est) perform under HTCM and Cell-wise contam.
12 HTCM Let s first illustrate through mini examples and diagrams: p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue)
13 HTCM Let s first illustrate through mini examples and diagrams: p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue)
14 HTCM Let s first illustrate through mini examples and diagrams: p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow)
15 HTCM Let s first illustrate through mini examples and diagrams: p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow), MVE (green)
16 HTCM Let s first illustrate through mini examples and diagrams: p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow), MVE (green), S-est. (red)
17 HTCM Let s first illustrate through mini examples and diagrams: p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow), MVE (green), S-est. (red),mm-est. (gray)
18 Davies S-estimator Definition (Davies, 1987): For µ R p and positive definite Σ, S-estimator is ( µ, Σ) = arg min s(µ, Σ) Σ = ŝ Σ where s(µ, Σ) is solution s to 1 n n (x i µ) T Σ 1 (x i µ) Σ 1/p ρ s = 1, i=1 with ρ( ) is some bounded monotone loss function and must satifies ( )) X E Φ (ρ = 1 c
19 MM-estimator (a two-stage estimator) Definition: For µ R p and positive definite Σ, MM-estimator is ( µ, Σ) = arg min J(µ, Σ) where J(µ, Σ) = 1 n n i=1 (x i µ) ρ T Σ 1 (x i µ) Σ 1/p ŝ n with ρ ( ) being a different loss function, i.e. ρ ( ) ρ 1 ( ) and ŝ n being the scale from S-estimate.
20 Cell-wise contamination p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue)
21 Cell-wise contamination p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow)
22 Cell-wise contamination p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow), MVE (green)
23 Cell-wise contamination p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow), MVE (green), S-est. (red)
24 Cell-wise contamination p = 3, n = 30, ɛ = 0.0, random covariance matrix, origin center, normal 95% conf. ellipsoids: MLE-clean (blue), MLE (yellow), MVE (green), S-est. (red),mm-est. (gray)
25 Composite S-estimator MVE, S-, and MM estimator performs very badly under cell-wise contam...
26 Composite S-estimator MVE, S-, and MM estimator performs very badly under cell-wise contam... Note that in our cell-wise contam. example, P( 1 variable is contam.) = 1 (1 ɛ) p =
27 Composite S-estimator MVE, S-, and MM estimator performs very badly under cell-wise contam... Note that in our cell-wise contam. example, P( 1 variable is contam.) = 1 (1 ɛ) p = In fact, all affine equivariant estimators for covariance collapse under cell-wise contam. (Allqalaf et al., 009)!
28 Composite S-estimator MVE, S-, and MM estimator performs very badly under cell-wise contam... Note that in our cell-wise contam. example, P( 1 variable is contam.) = 1 (1 ɛ) p = In fact, all affine equivariant estimators for covariance collapse under cell-wise contam. (Allqalaf et al., 009)! We need to develop a new estimator... Composite-S estimator (CSE)...but this estimator is not affine equivariant, which saves from falling under HTCM!
29 Composite S-estimator In short, CSE attempts to minimize the size of the covariance (e.g. ellipses ) for each pair of variables simultaneously, instead of all variables.
30 Composite S-estimator In short, CSE attempts to minimize the size of the covariance (e.g. ellipses ) for each pair of variables simultaneously, instead of all variables. It tries to downweight bivariate Mahalanobis distances, instead of full, when constructing the covariance matrix
31 Composite S-estimator In short, CSE attempts to minimize the size of the covariance (e.g. ellipses ) for each pair of variables simultaneously, instead of all variables. It tries to downweight bivariate Mahalanobis distances, instead of full, when constructing the covariance matrix Now let s have an example, we will get back to its definition later...
32 Composite S-estimator Example: p = 5, n = 100, ɛ = 0.10, random covariance matrix, origin center, normal, cell-wise contam. 95% confidence region based on Davies S-estimator vs true covariance: Scatter Plot Matrix V V V V V true S est
33 Composite S-estimator Example: p = 5, n = 100, ɛ = 0.10, random covariance matrix, origin center, normal, cell-wise contam. 95% confidence region based on CSE: Scatter Plot Matrix V V V V V true CSE
34 Composite S-estimator Example: p = 5, n = 100, ɛ = 0.10, random covariance matrix, origin center, normal, cell-wise contam. 95% confidence region based on CSE versus S-est. based on each pair: Scatter Plot Matrix V V V V V true CSE Pairwise S
35 Composite S-estimator Definition (CSE): For a given robust initial estimator Ω 0, ( µ, Σ) = arg min s(µ, Σ, Ω 0 ) Σ = ŝ Σ where s(µ, Σ, Ω 0 ) is solution s to d jk i p(p 1)n n p p 1 d jk (µ, Σ) i Σ jk 1/ ρ s c 0 Ω jk = 1 0 1/ i=1 j=k k=1 (µ, Σ) = (x jk µ jk ) T Σ jk 1 (x jk µ jk ) is the bivariate Mahalanobis distance, and c must satisifies the same criteria as in Davies S-estimator but in bivariate.
36 Composite MM-estimator CSE in general is robust under cell-wise contam. but not efficient.
37 Composite MM-estimator CSE in general is robust under cell-wise contam. but not efficient. Efficiency is a measurement of variability of the estimate relative to some gold standard, such as MLE, under no contamination.
38 Composite MM-estimator CSE in general is robust under cell-wise contam. but not efficient. Efficiency is a measurement of variability of the estimate relative to some gold standard, such as MLE, under no contamination. We use the corresponding MM-version (Tatsuoka and Tyler, 000) of CSE to achieve efficiency
39 Composite S- and MM-estimator Both have very nice but complex estimation procedure that closely link with S-estimator with missing data (Danilov et al, 01), but we will not describe here
40 Some results shown in ICORS 01 We performed a Monte Carlo study to assess the behavior of the proposed estimators. Simulation setting: x N(0, Σ 0 ), some n and p Σ 0 is exchangeable correlation, i.e. Σ 0 = 1 r... r r 1... r r... 1 r r... r 1
41 Some results shown in ICORS 01 Here we show some results for Correlations: r = 0.5 and r = 0.9 p = 10 and n = 100. p = 0 and n = 00.
42 Some results shown in ICORS 01 Performance criteria as: 1. Likelihood ratio test distance (LRT) for robustness evaluation D( Σ, Σ 0 ) = 1 N D( Σ i, Σ 0 ) N where i=1 D( Σ, Σ 0 ) = trace(σ 1 0 Σ) log(det(σ 1 0 Σ)) p. Relative efficiency based on LRT values for efficiency evaluation D( Σ MLE, Σ 0 )/D( Σ, Σ 0 )
43 Monte Carlo results Gaussian Efficiency Without Outliers p = 10, n = 100 p = 0, n = 00 ESTIMATES r S-est Pairwise-S CSE CMME ESTIMATES r S-est Pairwise-S CSE CMME
44 Monte Carlo results n = 100, p = 10, ɛ = 10% THCM Corr.=0.5 10% Contamination (n=100, p=10) Pairwise S Classical S CS (QC) CMM (QC) THCM Corr.= Average LRT distance 8 ICM Corr.=0.5 ICM Corr.= Outliers size
45 Remarks and conclusion In general, CSE (and CMME) are very robust under cell-wise contam. We have seen that CSE (and CMME) do not perform very well under HTCM Our goal is to have an estimator highly robust under both HTCM and cell-wise contam. (we are ambitious!)...while efficiency is our second priority To be continued...
46 Acknowledgement Special thanks to Professor R. Zamar and Professor V. Yohai! Prof. Zamar Prof. Yohai...AND THANK YOU FOR LISTENING! C. Agostinelli1, A. Leung,, V.J. Yohai3 and R.H. Zamar Development of robust scatter estimators under independent
Inference based on robust estimators Part 2
Inference based on robust estimators Part 2 Matias Salibian-Barrera 1 Department of Statistics University of British Columbia ECARES - Dec 2007 Matias Salibian-Barrera (UBC) Robust inference (2) ECARES
More informationA SHORT COURSE ON ROBUST STATISTICS. David E. Tyler Rutgers The State University of New Jersey. Web-Site dtyler/shortcourse.
A SHORT COURSE ON ROBUST STATISTICS David E. Tyler Rutgers The State University of New Jersey Web-Site www.rci.rutgers.edu/ dtyler/shortcourse.pdf References Huber, P.J. (1981). Robust Statistics. Wiley,
More informationIMPROVING THE SMALL-SAMPLE EFFICIENCY OF A ROBUST CORRELATION MATRIX: A NOTE
IMPROVING THE SMALL-SAMPLE EFFICIENCY OF A ROBUST CORRELATION MATRIX: A NOTE Eric Blankmeyer Department of Finance and Economics McCoy College of Business Administration Texas State University San Marcos
More informationRobust estimation of scale and covariance with P n and its application to precision matrix estimation
Robust estimation of scale and covariance with P n and its application to precision matrix estimation Garth Tarr, Samuel Müller and Neville Weber USYD 2013 School of Mathematics and Statistics THE UNIVERSITY
More informationStahel-Donoho Estimation for High-Dimensional Data
Stahel-Donoho Estimation for High-Dimensional Data Stefan Van Aelst KULeuven, Department of Mathematics, Section of Statistics Celestijnenlaan 200B, B-3001 Leuven, Belgium Email: Stefan.VanAelst@wis.kuleuven.be
More informationIntroduction to Robust Statistics. Elvezio Ronchetti. Department of Econometrics University of Geneva Switzerland.
Introduction to Robust Statistics Elvezio Ronchetti Department of Econometrics University of Geneva Switzerland Elvezio.Ronchetti@metri.unige.ch http://www.unige.ch/ses/metri/ronchetti/ 1 Outline Introduction
More informationAccurate and Powerful Multivariate Outlier Detection
Int. Statistical Inst.: Proc. 58th World Statistical Congress, 11, Dublin (Session CPS66) p.568 Accurate and Powerful Multivariate Outlier Detection Cerioli, Andrea Università di Parma, Dipartimento di
More informationA ROBUST METHOD OF ESTIMATING COVARIANCE MATRIX IN MULTIVARIATE DATA ANALYSIS G.M. OYEYEMI *, R.A. IPINYOMI **
ANALELE ŞTIINłIFICE ALE UNIVERSITĂłII ALEXANDRU IOAN CUZA DIN IAŞI Tomul LVI ŞtiinŃe Economice 9 A ROBUST METHOD OF ESTIMATING COVARIANCE MATRIX IN MULTIVARIATE DATA ANALYSIS G.M. OYEYEMI, R.A. IPINYOMI
More informationMULTIVARIATE TECHNIQUES, ROBUSTNESS
MULTIVARIATE TECHNIQUES, ROBUSTNESS Mia Hubert Associate Professor, Department of Mathematics and L-STAT Katholieke Universiteit Leuven, Belgium mia.hubert@wis.kuleuven.be Peter J. Rousseeuw 1 Senior Researcher,
More informationTITLE : Robust Control Charts for Monitoring Process Mean of. Phase-I Multivariate Individual Observations AUTHORS : Asokan Mulayath Variyath.
TITLE : Robust Control Charts for Monitoring Process Mean of Phase-I Multivariate Individual Observations AUTHORS : Asokan Mulayath Variyath Department of Mathematics and Statistics, Memorial University
More informationRe-weighted Robust Control Charts for Individual Observations
Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia 426 Re-weighted Robust Control Charts for Individual Observations Mandana Mohammadi 1, Habshah Midi 1,2 and Jayanthi Arasan 1,2 1 Laboratory of Applied
More informationRobust and sparse Gaussian graphical modelling under cell-wise contamination
Robust and sparse Gaussian graphical modelling under cell-wise contamination Shota Katayama 1, Hironori Fujisawa 2 and Mathias Drton 3 1 Tokyo Institute of Technology, Japan 2 The Institute of Statistical
More informationJournal of Statistical Software
JSS Journal of Statistical Software April 2013, Volume 53, Issue 3. http://www.jstatsoft.org/ Fast and Robust Bootstrap for Multivariate Inference: The R Package FRB Stefan Van Aelst Ghent University Gert
More informationRobust Exponential Smoothing of Multivariate Time Series
Robust Exponential Smoothing of Multivariate Time Series Christophe Croux,a, Sarah Gelper b, Koen Mahieu a a Faculty of Business and Economics, K.U.Leuven, Naamsestraat 69, 3000 Leuven, Belgium b Erasmus
More informationON THE CALCULATION OF A ROBUST S-ESTIMATOR OF A COVARIANCE MATRIX
STATISTICS IN MEDICINE Statist. Med. 17, 2685 2695 (1998) ON THE CALCULATION OF A ROBUST S-ESTIMATOR OF A COVARIANCE MATRIX N. A. CAMPBELL *, H. P. LOPUHAA AND P. J. ROUSSEEUW CSIRO Mathematical and Information
More informationRobust Tools for the Imperfect World
Robust Tools for the Imperfect World Peter Filzmoser a,, Valentin Todorov b a Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria
More informationRobust Inference for Seemingly Unrelated Regression Models
Robust Inference for Seemingly Unrelated Regression Models arxiv:1801.04716v3 [stat.me] 14 May 2018 Kris Peremans and Stefan Van Aelst Department of Mathematics, KU Leuven, 3001 Leuven, Belgium May 15,
More information1. Density and properties Brief outline 2. Sampling from multivariate normal and MLE 3. Sampling distribution and large sample behavior of X and S 4.
Multivariate normal distribution Reading: AMSA: pages 149-200 Multivariate Analysis, Spring 2016 Institute of Statistics, National Chiao Tung University March 1, 2016 1. Density and properties Brief outline
More informationRobust scale estimation with extensions
Robust scale estimation with extensions Garth Tarr, Samuel Müller and Neville Weber School of Mathematics and Statistics THE UNIVERSITY OF SYDNEY Outline The robust scale estimator P n Robust covariance
More informationRobust Wilks' Statistic based on RMCD for One-Way Multivariate Analysis of Variance (MANOVA)
ISSN 2224-584 (Paper) ISSN 2225-522 (Online) Vol.7, No.2, 27 Robust Wils' Statistic based on RMCD for One-Way Multivariate Analysis of Variance (MANOVA) Abdullah A. Ameen and Osama H. Abbas Department
More informationIdentification of Multivariate Outliers: A Performance Study
AUSTRIAN JOURNAL OF STATISTICS Volume 34 (2005), Number 2, 127 138 Identification of Multivariate Outliers: A Performance Study Peter Filzmoser Vienna University of Technology, Austria Abstract: Three
More informationThe minimum volume ellipsoid (MVE), introduced
Minimum volume ellipsoid Stefan Van Aelst 1 and Peter Rousseeuw 2 The minimum volume ellipsoid (MVE) estimator is based on the smallest volume ellipsoid that covers h of the n observations. It is an affine
More informationResearch Article Robust Multivariate Control Charts to Detect Small Shifts in Mean
Mathematical Problems in Engineering Volume 011, Article ID 93463, 19 pages doi:.1155/011/93463 Research Article Robust Multivariate Control Charts to Detect Small Shifts in Mean Habshah Midi 1, and Ashkan
More informationComputational Connections Between Robust Multivariate Analysis and Clustering
1 Computational Connections Between Robust Multivariate Analysis and Clustering David M. Rocke 1 and David L. Woodruff 2 1 Department of Applied Science, University of California at Davis, Davis, CA 95616,
More informationIntroduction to Robust Statistics. Anthony Atkinson, London School of Economics, UK Marco Riani, Univ. of Parma, Italy
Introduction to Robust Statistics Anthony Atkinson, London School of Economics, UK Marco Riani, Univ. of Parma, Italy Multivariate analysis Multivariate location and scatter Data where the observations
More informationWEIGHTED LIKELIHOOD NEGATIVE BINOMIAL REGRESSION
WEIGHTED LIKELIHOOD NEGATIVE BINOMIAL REGRESSION Michael Amiguet 1, Alfio Marazzi 1, Victor Yohai 2 1 - University of Lausanne, Institute for Social and Preventive Medicine, Lausanne, Switzerland 2 - University
More informationSupplementary Material for Wang and Serfling paper
Supplementary Material for Wang and Serfling paper March 6, 2017 1 Simulation study Here we provide a simulation study to compare empirically the masking and swamping robustness of our selected outlyingness
More informationLeverage effects on Robust Regression Estimators
Leverage effects on Robust Regression Estimators David Adedia 1 Atinuke Adebanji 2 Simon Kojo Appiah 2 1. Department of Basic Sciences, School of Basic and Biomedical Sciences, University of Health and
More informationThe Performance of Mutual Information for Mixture of Bivariate Normal Distributions Based on Robust Kernel Estimation
Applied Mathematical Sciences, Vol. 4, 2010, no. 29, 1417-1436 The Performance of Mutual Information for Mixture of Bivariate Normal Distributions Based on Robust Kernel Estimation Kourosh Dadkhah 1 and
More informationodhady a jejich (ekonometrické)
modifikace Stochastic Modelling in Economics and Finance 2 Advisor : Prof. RNDr. Jan Ámos Víšek, CSc. Petr Jonáš 27 th April 2009 Contents 1 2 3 4 29 1 In classical approach we deal with stringent stochastic
More informationRobust Linear Discriminant Analysis and the Projection Pursuit Approach
Robust Linear Discriminant Analysis and the Projection Pursuit Approach Practical aspects A. M. Pires 1 Department of Mathematics and Applied Mathematics Centre, Technical University of Lisbon (IST), Lisboa,
More informationROBUST ESTIMATION OF A CORRELATION COEFFICIENT: AN ATTEMPT OF SURVEY
ROBUST ESTIMATION OF A CORRELATION COEFFICIENT: AN ATTEMPT OF SURVEY G.L. Shevlyakov, P.O. Smirnov St. Petersburg State Polytechnic University St.Petersburg, RUSSIA E-mail: Georgy.Shevlyakov@gmail.com
More informationAPPLICATIONS OF A ROBUST DISPERSION ESTIMATOR. Jianfeng Zhang. Doctor of Philosophy in Mathematics, Southern Illinois University, 2011
APPLICATIONS OF A ROBUST DISPERSION ESTIMATOR by Jianfeng Zhang Doctor of Philosophy in Mathematics, Southern Illinois University, 2011 A Research Dissertation Submitted in Partial Fulfillment of the Requirements
More informationProjection Estimators for Generalized Linear Models
Projection Estimators for Generalized Linear Models Andrea Bergesio and Victor J. Yohai Andrea Bergesio is Teaching Assistant, Universidad Nacional del Litoral, IMAL, Güemes 3450, S3000GLN Santa Fe, Argentina
More informationDetecting Deviating Data Cells
TECHNOMETRICS 2018, VOL. 60, NO. 2, 135 145 https://doi.org/10.1080/00401706.2017.1340909 Detecting Deviating Data Cells Peter J. Rousseeuw and Wannes Van Den Bossche Department of Mathematics, KU Leuven,
More informationparameter space Θ, depending only on X, such that Note: it is not θ that is random, but the set C(X).
4. Interval estimation The goal for interval estimation is to specify the accurary of an estimate. A 1 α confidence set for a parameter θ is a set C(X) in the parameter space Θ, depending only on X, such
More informationKANSAS STATE UNIVERSITY
ROBUST MIXTURES OF REGRESSION MODELS by XIUQIN BAI M.S., Kansas State University, USA, 2010 AN ABSTRACT OF A DISSERTATION submitted in partial fulfillment of the requirements for the degree DOCTOR OF PHILOSOPHY
More informationRobust and sparse estimation of the inverse covariance matrix using rank correlation measures
Robust and sparse estimation of the inverse covariance matrix using rank correlation measures Christophe Croux, Viktoria Öllerer Abstract Spearman s rank correlation is a robust alternative for the standard
More informationRobust Estimation of Cronbach s Alpha
Robust Estimation of Cronbach s Alpha A. Christmann University of Dortmund, Fachbereich Statistik, 44421 Dortmund, Germany. S. Van Aelst Ghent University (UGENT), Department of Applied Mathematics and
More informationDepartamento de Estadfstica y Econometrfa Statistics and Econometrics Series 10
Working Paper 94-22 Departamento de Estadfstica y Econometrfa Statistics and Econometrics Series 10 Universidad Carlos III de Madrid June 1994 CaUe Madrid, 126 28903 Getafe (Spain) Fax (341) 624-9849 A
More informationRobust estimation in time series
Robust estimation in time series V. A. Reisen NuMes, D.E; Universidade Federal do Espirito Santo, Brazil. 20 de setembro de 2011 Contents Abstract Introduction-Co-authors references Introduction-Examples
More informationThe S-estimator of multivariate location and scatter in Stata
The Stata Journal (yyyy) vv, Number ii, pp. 1 9 The S-estimator of multivariate location and scatter in Stata Vincenzo Verardi University of Namur (FUNDP) Center for Research in the Economics of Development
More informationOn consistency factors and efficiency of robust S-estimators
TEST DOI.7/s749-4-357-7 ORIGINAL PAPER On consistency factors and efficiency of robust S-estimators Marco Riani Andrea Cerioli Francesca Torti Received: 5 May 3 / Accepted: 6 January 4 Sociedad de Estadística
More informationDetection of outliers in multivariate data:
1 Detection of outliers in multivariate data: a method based on clustering and robust estimators Carla M. Santos-Pereira 1 and Ana M. Pires 2 1 Universidade Portucalense Infante D. Henrique, Oporto, Portugal
More informationVienna University of Technology
Vienna University of Technology Deliverable 4. Final Report Contract with the world bank (1157976) Detecting outliers in household consumption survey data Peter Filzmoser Authors: Johannes Gussenbauer
More informationTwo Simple Resistant Regression Estimators
Two Simple Resistant Regression Estimators David J. Olive Southern Illinois University January 13, 2005 Abstract Two simple resistant regression estimators with O P (n 1/2 ) convergence rate are presented.
More informationON THE MAXIMUM BIAS FUNCTIONS OF MM-ESTIMATES AND CONSTRAINED M-ESTIMATES OF REGRESSION
ON THE MAXIMUM BIAS FUNCTIONS OF MM-ESTIMATES AND CONSTRAINED M-ESTIMATES OF REGRESSION BY JOSE R. BERRENDERO, BEATRIZ V.M. MENDES AND DAVID E. TYLER 1 Universidad Autonoma de Madrid, Federal University
More informationWhy the Rousseeuw Yohai Paradigm is One of the Largest and Longest Running Scientific Hoaxes in History
Why the Rousseeuw Yohai Paradigm is One of the Largest and Longest Running Scientific Hoaxes in History David J. Olive Southern Illinois University December 4, 2012 Abstract The Rousseeuw Yohai paradigm
More informationRobustness for dummies
Robustness for dummies CRED WP 2012/06 Vincenzo Verardi, Marjorie Gassner and Darwin Ugarte Center for Research in the Economics of Development University of Namur Robustness for dummies Vincenzo Verardi,
More informationCPSC 540: Machine Learning
CPSC 540: Machine Learning Expectation Maximization Mark Schmidt University of British Columbia Winter 2018 Last Time: Learning with MAR Values We discussed learning with missing at random values in data:
More informationarxiv: v1 [math.st] 11 Jun 2018
Robust test statistics for the two-way MANOVA based on the minimum covariance determinant estimator Bernhard Spangl a, arxiv:1806.04106v1 [math.st] 11 Jun 2018 a Institute of Applied Statistics and Computing,
More informationA Modified M-estimator for the Detection of Outliers
A Modified M-estimator for the Detection of Outliers Asad Ali Department of Statistics, University of Peshawar NWFP, Pakistan Email: asad_yousafzay@yahoo.com Muhammad F. Qadir Department of Statistics,
More informationRobust negative binomial regression
Robust negative binomial regression Michel Amiguet, Alfio Marazzi, Marina S. Valdora, Víctor J. Yohai september 2016 Negative Binomial Regression Poisson regression is the standard method used to model
More informationImproved Feasible Solution Algorithms for. High Breakdown Estimation. Douglas M. Hawkins. David J. Olive. Department of Applied Statistics
Improved Feasible Solution Algorithms for High Breakdown Estimation Douglas M. Hawkins David J. Olive Department of Applied Statistics University of Minnesota St Paul, MN 55108 Abstract High breakdown
More informationParallel Computation of High Dimensional Robust Correlation and Covariance Matrices
Parallel Computation of High Dimensional Robust Correlation and Covariance Matrices James Chilson, Raymond Ng, Alan Wagner Department of Computer Science University of British Columbia Vancouver, BC, Canada,
More informationRobust estimation for linear regression with asymmetric errors with applications to log-gamma regression
Robust estimation for linear regression with asymmetric errors with applications to log-gamma regression Ana M. Bianco, Marta García Ben and Víctor J. Yohai Universidad de Buenps Aires October 24, 2003
More informationSmall Sample Corrections for LTS and MCD
myjournal manuscript No. (will be inserted by the editor) Small Sample Corrections for LTS and MCD G. Pison, S. Van Aelst, and G. Willems Department of Mathematics and Computer Science, Universitaire Instelling
More informationMaximum Likelihood Estimation; Robust Maximum Likelihood; Missing Data with Maximum Likelihood
Maximum Likelihood Estimation; Robust Maximum Likelihood; Missing Data with Maximum Likelihood PRE 906: Structural Equation Modeling Lecture #3 February 4, 2015 PRE 906, SEM: Estimation Today s Class An
More informationMinimum Regularized Covariance Determinant Estimator
Minimum Regularized Covariance Determinant Estimator Honey, we shrunk the data and the covariance matrix Kris Boudt (joint with: P. Rousseeuw, S. Vanduffel and T. Verdonck) Vrije Universiteit Brussel/Amsterdam
More informationRobust canonical correlation analysis: a predictive approach.
Robust canonical correlation analysis: a predictive approach. Nadia L. Kudraszow a, Ricardo A. Maronna b a CONICET and University of La Plata, Argentina b University of La Plata, Argentina Abstract We
More informationIntroduction Robust regression Examples Conclusion. Robust regression. Jiří Franc
Robust regression Robust estimation of regression coefficients in linear regression model Jiří Franc Czech Technical University Faculty of Nuclear Sciences and Physical Engineering Department of Mathematics
More information368 XUMING HE AND GANG WANG of convergence for the MVE estimator is n ;1=3. We establish strong consistency and functional continuity of the MVE estim
Statistica Sinica 6(1996), 367-374 CROSS-CHECKING USING THE MINIMUM VOLUME ELLIPSOID ESTIMATOR Xuming He and Gang Wang University of Illinois and Depaul University Abstract: We show that for a wide class
More informationRobustifying Robust Estimators
Robustifying Robust Estimators David J. Olive and Douglas M. Hawkins Southern Illinois University and University of Minnesota January 12, 2007 Abstract In the literature, estimators for regression or multivariate
More informationINVARIANT COORDINATE SELECTION
INVARIANT COORDINATE SELECTION By David E. Tyler 1, Frank Critchley, Lutz Dümbgen 2, and Hannu Oja Rutgers University, Open University, University of Berne and University of Tampere SUMMARY A general method
More informationOutlier Detection via Feature Selection Algorithms in
Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS032) p.4638 Outlier Detection via Feature Selection Algorithms in Covariance Estimation Menjoge, Rajiv S. M.I.T.,
More informationFast and robust bootstrap for LTS
Fast and robust bootstrap for LTS Gert Willems a,, Stefan Van Aelst b a Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, B-2020 Antwerp, Belgium b Department of
More information1 Robust statistics; Examples and Introduction
Robust Statistics Laurie Davies 1 and Ursula Gather 2 1 Department of Mathematics, University of Essen, 45117 Essen, Germany, laurie.davies@uni-essen.de 2 Department of Statistics, University of Dortmund,
More informationRobust Estimation, Regression and Ranking with Applications in Portfolio Optimization. Tri-Dung Nguyen
Robust Estimation, Regression and Ranking with Applications in Portfolio Optimization by Tri-Dung Nguyen Submitted to the Sloan School of Management in partial fulfillment of the requirements for the degree
More informationIntroduction to robust statistics*
Introduction to robust statistics* Xuming He National University of Singapore To statisticians, the model, data and methodology are essential. Their job is to propose statistical procedures and evaluate
More informationDetection of Multivariate Outliers in Business Survey Data with Incomplete Information
Noname manuscript No. (will be inserted by the editor) Detection of Multivariate Outliers in Business Survey Data with Incomplete Information Valentin Todorov Matthias Templ Peter Filzmoser Received: date
More informationResearch Article Robust Control Charts for Monitoring Process Mean of Phase-I Multivariate Individual Observations
Journal of Quality and Reliability Engineering Volume 3, Article ID 4, 4 pages http://dx.doi.org/./3/4 Research Article Robust Control Charts for Monitoring Process Mean of Phase-I Multivariate Individual
More informationReinforcement Learning and Optimal Control. ASU, CSE 691, Winter 2019
Reinforcement Learning and Optimal Control ASU, CSE 691, Winter 2019 Dimitri P. Bertsekas dimitrib@mit.edu Lecture 8 Bertsekas Reinforcement Learning 1 / 21 Outline 1 Review of Infinite Horizon Problems
More informationApproximate Median Regression via the Box-Cox Transformation
Approximate Median Regression via the Box-Cox Transformation Garrett M. Fitzmaurice,StuartR.Lipsitz, and Michael Parzen Median regression is used increasingly in many different areas of applications. The
More informationComputationally Easy Outlier Detection via Projection Pursuit with Finitely Many Directions
Computationally Easy Outlier Detection via Projection Pursuit with Finitely Many Directions Robert Serfling 1 and Satyaki Mazumder 2 University of Texas at Dallas and Indian Institute of Science, Education
More informationRobust regression in R. Eva Cantoni
Robust regression in R Eva Cantoni Research Center for Statistics and Geneva School of Economics and Management, University of Geneva, Switzerland April 4th, 2017 1 Robust statistics philosopy 2 Robust
More informationASYMPTOTICS OF REWEIGHTED ESTIMATORS OF MULTIVARIATE LOCATION AND SCATTER. By Hendrik P. Lopuhaä Delft University of Technology
The Annals of Statistics 1999, Vol. 27, No. 5, 1638 1665 ASYMPTOTICS OF REWEIGHTED ESTIMATORS OF MULTIVARIATE LOCATION AND SCATTER By Hendrik P. Lopuhaä Delft University of Technology We investigate the
More informationFast and Robust Discriminant Analysis
Fast and Robust Discriminant Analysis Mia Hubert a,1, Katrien Van Driessen b a Department of Mathematics, Katholieke Universiteit Leuven, W. De Croylaan 54, B-3001 Leuven. b UFSIA-RUCA Faculty of Applied
More informationRobust regression in Stata
The Stata Journal (2009) 9, Number 3, pp. 439 453 Robust regression in Stata Vincenzo Verardi 1 University of Namur (CRED) and Université Libre de Bruxelles (ECARES and CKE) Rempart de la Vierge 8, B-5000
More informationRobust M-Estimation of Multivariate GARCH Models
Robust M-Estimation of Multivariate GARCH Models Kris Boudt Christophe Croux Faculty of Business and Economics, K.U.Leuven, Belgium Abstract In empirical work on multivariate financial time series, it
More informationMedian Cross-Validation
Median Cross-Validation Chi-Wai Yu 1, and Bertrand Clarke 2 1 Department of Mathematics Hong Kong University of Science and Technology 2 Department of Medicine University of Miami IISA 2011 Outline Motivational
More informationROBUSTNESS OF TWO-PHASE REGRESSION TESTS
REVSTAT Statistical Journal Volume 3, Number 1, June 2005, 1 18 ROBUSTNESS OF TWO-PHASE REGRESSION TESTS Authors: Carlos A.R. Diniz Departamento de Estatística, Universidade Federal de São Carlos, São
More informationA Robust Approach to Regularized Discriminant Analysis
A Robust Approach to Regularized Discriminant Analysis Moritz Gschwandtner Department of Statistics and Probability Theory Vienna University of Technology, Austria Österreichische Statistiktage, Graz,
More informationDr. Allen Back. Sep. 23, 2016
Dr. Allen Back Sep. 23, 2016 Look at All the Data Graphically A Famous Example: The Challenger Tragedy Look at All the Data Graphically A Famous Example: The Challenger Tragedy Type of Data Looked at the
More informationMultivariate coefficient of variation for functional data
Multivariate coefficient of variation for functional data Mirosław Krzyśko 1 and Łukasz Smaga 2 1 Interfaculty Institute of Mathematics and Statistics The President Stanisław Wojciechowski State University
More informationLikelihood-based inference with missing data under missing-at-random
Likelihood-based inference with missing data under missing-at-random Jae-kwang Kim Joint work with Shu Yang Department of Statistics, Iowa State University May 4, 014 Outline 1. Introduction. Parametric
More informationOutlier detection for skewed data
Outlier detection for skewed data Mia Hubert 1 and Stephan Van der Veeken December 7, 27 Abstract Most outlier detection rules for multivariate data are based on the assumption of elliptical symmetry of
More informationRegression Analysis for Data Containing Outliers and High Leverage Points
Alabama Journal of Mathematics 39 (2015) ISSN 2373-0404 Regression Analysis for Data Containing Outliers and High Leverage Points Asim Kumer Dey Department of Mathematics Lamar University Md. Amir Hossain
More informationRobust Linear Model Selection for High-Dimensional Batasets
Robust Linear Model Selection for High-Dimensional Batasets by MD JAFAR AHMED KHAN M.Sc., The University of British Columbia, 2002 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE
More informationAn Alternative Hotelling T 2 Control Chart Based on Minimum Vector Variance (MVV)
www.ccsenet.org/mas Modern Applied cience Vol. 5, No. 4; August 011 An Alternative Hotelling T Control Chart Based on Minimum Vector Variance () haripah..yahaya College of Art and ciences, Universiti Utara
More informationRobust estimation for the multivariate linear model based on a τ-scale
Journal of Multivariate Analysis 97 2006) 1600 1622 www.elsevier.com/locate/mva Robust estimation for the multivariate linear model based on a τ-scale Marta García Ben a, Elena Martínez a, Víctor J. Yohai
More informationTo Peter, Kris, Dad, Lame and everyone else who believed in me. ii
Multivariate Outlier Detection and Robust Clustering with Minimum Covariance Determinant Estimation and S-Estimation By Johanna Sarah Hardin B.A. (Pomona College) 1995 M.S. (University of California, Davis)
More informationLecture 3. Inference about multivariate normal distribution
Lecture 3. Inference about multivariate normal distribution 3.1 Point and Interval Estimation Let X 1,..., X n be i.i.d. N p (µ, Σ). We are interested in evaluation of the maximum likelihood estimates
More informationS estimators for functional principal component analysis
S estimators for functional principal component analysis Graciela Boente and Matías Salibián Barrera Abstract Principal components analysis is a widely used technique that provides an optimal lower-dimensional
More informationRobust Variable Selection Through MAVE
Robust Variable Selection Through MAVE Weixin Yao and Qin Wang Abstract Dimension reduction and variable selection play important roles in high dimensional data analysis. Wang and Yin (2008) proposed sparse
More informationRobust estimators for additive models using backfitting
Robust estimators for additive models using backfitting Graciela Boente Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Argentina Alejandra Martínez Facultad de Ciencias
More informationSymmetrised M-estimators of multivariate scatter
Journal of Multivariate Analysis 98 (007) 1611 169 www.elsevier.com/locate/jmva Symmetrised M-estimators of multivariate scatter Seija Sirkiä a,, Sara Taskinen a, Hannu Oja b a Department of Mathematics
More informationS estimators for functional principal component analysis
S estimators for functional principal component analysis Graciela Boente and Matías Salibián Barrera Abstract Principal components analysis is a widely used technique that provides an optimal lower-dimensional
More informationOptimal robust estimates using the Hellinger distance
Adv Data Anal Classi DOI 10.1007/s11634-010-0061-8 REGULAR ARTICLE Optimal robust estimates using the Hellinger distance Alio Marazzi Victor J. Yohai Received: 23 November 2009 / Revised: 25 February 2010
More informationJ. W. LEE (Kumoh Institute of Technology, Kumi, South Korea) V. I. SHIN (Gwangju Institute of Science and Technology, Gwangju, South Korea)
J. W. LEE (Kumoh Institute of Technology, Kumi, South Korea) V. I. SHIN (Gwangju Institute of Science and Technology, Gwangju, South Korea) G. L. SHEVLYAKOV (Gwangju Institute of Science and Technology,
More informationRobust covariance matrices estimation and applications in signal processing
Robust covariance matrices estimation and applications in signal processing F. Pascal SONDRA/Supelec GDR ISIS Journée Estimation et traitement statistique en grande dimension May 16 th, 2013 FP (SONDRA/Supelec)
More information