Preprints

by David Olive
        Preprint M-02-006
        Copyright May 2003, June 2008

  • This preprint provides some of the most important theory in the field of robust statistics. The paper shows that a simple modification to the most used but inconsistent algorithms for robust statistics results in easily computed sqrt n consistent high breakdown estimators.
  • High Breakdown Multivariate Estimators hbrs.pdf
  • Goodness of Fit Plots for Binomial and Poisson Regression gfit.pdf
  • OLS for Single Index and 1D Regression Models sindx.pdf
  • The Breakdown of Breakdown bdbd.pdf
  • Inference for the Pareto, Half Normal and Power Distributions pareto.pdf
  • The following preprints have not yet been submitted or need to be resubmitted.
  • Robust Covariance Matrix Estimation rcovm.pdf
  • Plots for Survival Regression sreg.pdf
  • Response Plots for Experimental Design rploted.pdf
  • Comments on Breakdown bkdn.pdf
  • 1D Regression onedreg.pdf
  • Graphical Aids for Regression. gaid.pdf
  • A Simple Plot for Model Assessment simp.pdf
  • Prediction intervals in the presence of outliers pi.pdf
  • (long version of) Robustifying Robust Estimators lconc.pdf
  • For location scale families, estimators based on the median and mad have optimal robustness properties. Use He's cross checking technique to make an asymptoticaly efficient estimator.
  • Robust Estimators for Transformed Location-Scale Families. robloc.pdf
  • The following four preprints have been cited by other authors.
  • Chang, J., and Olive, D.J. (2007), Resistant Dimension Reduction resdr.pdf
  • Olive, D.J., and Hawkins, D.M. (2007), Robustifying Robust Estimators, preprint available from www.math.siu.edu/olive/ppconc.pdf
  • Olive, D.J. (2005), A Simple Confidence Interval for the Median, preprint available from www.math.siu.edu/olive/ppmedci.pdf
  • The June 2008 ROBUST STATISTICS NOTES are below. PLEASE CITE THIS WORK IF YOU USE IT.
  • Olive, D.J. (2008), Applied Robust Statistics, preprint available from (www.math.siu.edu/olive/run.pdf). robnotes.pdf
  • Web page with data sets and programs to go with the course notes. robust.html
  • Some statistical inference preprints, including the ONLINE TEXT
  • Olive, D.J. (2008), A Course in Statistical Theory, preprint available from (www.math.siu.edu/olive/infer.htm), are here. infer.htm
  • The next four documents may be of mild interest, but will probably never be published.
  • Slides for Response Plots for Experimental Design Talk srplot.pdf
  • This 1996 result grew into a 2002 JASA discussion paper. dense.pdf
  • This 1997 result on partitioning may be of mild interest. part.pdf
  • Slides for Visualizing 1D Regression, presented at ICORS 2003. slides.pdf
  • The following are preprints of published or accepted papers.
  • Olive, D.J. and Hawkins, D.M. (2007), Behavior of Elemental Sets in Regression, Statistics and Probability Letters, 77, 621-624. elem.pdf
  • This paper shows how to construct asymptotically optimal prediction intervals for regression models of the form Y = m(x) + e. The errors need to be iid unimodal and emphasis is on linear regression.
  • Olive, D.J. (2007), Prediction Intervals for Regression Models, Computational Statistics and Data Analysis, 51, 3115-3122. spi.pdf
  • This paper shows that the variable selection software originally meant for multiple linear regression gives useful results for a much larger class of models, including generalized linear models and single index models, if the Mallows' Cp criterion is used. For models I with k predictors, the screen Cp(I) < 2k is much more effective than the screen Cp(I) < k.
  • Olive, D.J. and Hawkins, D.M. (2005), Variable Selection for 1D Regression Models, Technometrics, 47, 43-50. varsel.pdf
  • Olive, D.J. (2005), Two Simple Resistant Regression Estimators, Computational Statistics and Data Analysis, 49, 809-819. mba.pdf
  • The MBA estimator is not as good as the FCH estimator in "High Breakdown Robust Estimators," but was the first easily computed estimator of multivariate location and dipsersion shown (in 2004) to be sqrt(n) consistent and high breakdown. See "Robustifying Robust Estimators" or "Applied Robust Statistics" for proofs.
  • Olive, D.J. (2004), A Resistant Estimator of Multivariate Location and Dispersion, Computational Statistics and Data Analysis, 46, 99-102. rcov.pdf
  • The following paper suggests ways to robustify regression techniques for single index models and sliced inverse regression.
  • Olive, D.J. (2004), Visualizing 1D Regression, in Theory and Applications of Recent Robust Methods, edited by M. Hubert, G. Pison, A. Struyf and S. Van Aelst, Series: Statistics for Industry and Technology, Birkhauser, Basel, 221-233. vreg.pdf
  • Olive, D.J., and Hawkins, D.M. (2003), Robust Regression with High Coverage, Statistics and Probability Letters, 63, 259-266. hcov.pdf
  • The following paper provides a simultaneous diagnostic for whether the data follows a multivariate normal distribution or some other elliptically contoured distribution. It also provides a nice way to estimate and visualize single index models.
  • Olive, D.J. (2002), Applications of Robust Distances for Regression, Technometrics, 44, 64-71. rdist.pdf
  • The following paper gives extremely important theoretical results. It shows that software implementations for estimators of robust regression and robust multivariate location and dispersion tend to be inconsistent with zero breakdown value. The commonly used elemental basic resampling algorithm draws K elemental sets. Each elemental fit is inconsistent, so the final estimator is inconsistent, regardless of how the algorithm chooses the elemental fit. The CM, GS, LMS, LQD, LTS, maximum depth, MCD, MVE, one step GM and GR, projection, S, tau, t type, and many other robust estimators are of little applied interest because they are impractical to compute. The "Robustifying Robust Estimators" paper shows how modify some algorithms so that the resulting estimators are easily computed sqrt n consistent high breakdown estimators.
  • Hawkins, D.M., and Olive, D.J. (2002), Inconsistency of Resampling Algorithms for High Breakdown Regression Estimators and a New Algorithm (with discussion), Journal of the American Statistical Association, 97, 136-148. incon.pdf
  • This paper gives a graphical method for estimating response transformations that can be used to complement or replace the numerical Box-Cox method.
  • Cook, R.D., and Olive, D.J. (2001), A Note on Visualizing Response Transformations, Technometrics, 43, 443-449. resp.pdf
  • Olive, D.J. (2001), High Breakdown Analogs of the Trimmed Mean, Statistics and Probability Letters, 51, 87-92.rloc.pdf
  • Hawkins, D.M., and Olive, D.J. (1999), Improved Feasible Solution Algorithms for High Breakdown Estimation, Computational Statistics and Data Analysis, 30, 1-11. ifsa.pdf
  • Hawkins, D.M., and Olive, D. (1999), Applications and Algorithms for Least Trimmed Sum of Absolute Deviations Regression, Computational Statistics and Data Analysis, 32, 119-134. lta.pdf

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