- Robustifying Robust Estimators David J. Olive and Douglas M. Hawkins
- Robust Regression with High Coverage David J. Olive and Douglas M. Hawkins
- 1D Regression ... estimates of the linear regression coefficients are relevant to the linear
- Math 250 Exam 4 review. Thursday April 30. Bring a TI30 calculator but NO NOTES. Emphasis on sections 5.5, 6.1, 6.2, 6.3, 3.7, 6.6, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6,
- A Simple Plot for Model Assessment David J. Olive
- A Course in Statistical Theory David J. Olive
- 1D Regression David J. Olive
- Multiple Linear and 1D Regression David J. Olive
- Two Simple Resistant Regression Estimators David J. Olive
- WLS and Generalized Least 4.1 Random Vectors
- Applied Robust Statistics David J. Olive
- Asymptotically Optimal Prediction David J. Olive
- BIBLIOGRAPHY 517 1. Abraham, B., and Ledolter, J. (2006), Introduction to Regression Mod-
- Plots for Survival Regression David J. Olive
- Course Announcement Spring 2007, MATH 473: Reliability and Survival Models: MWF 10:00-10:50 WHAM 0312
- Math 250 Exam 2 review. Thursday March 5. Bring a TI30 calculator but NO NOTES. Emphasis on sections 5.5, 6.1, 6.2, 6.3, 3.7, 6.6, 8.1, 8.2, 8.3, part of 8.4; HW1-
- Multiple Linear and 1D Regression David J. Olive
- Some Transformed Distributions Hassan Abuhassan
- Stuff for Students To be blunt, many of us are lousy teachers, and our efforts to improve are
- Applied Robust Statistics David J. Olive
- A Simple Limit Theorem for Exponential Families David J. Olive
- Exponential Families 3.1 Regular Exponential Families
- Improved Feasible Solution Algorithms for High Breakdown Estimation
- OLS for 1D Regression Models Jing Chang David J. Olive
- Confidence Intervals 9.1 Introduction
- Exam 3 Review Suppose that Xi = x = (x1, ..., xk)T
- PLOTS FOR THE DESIGN AND ANALYSIS OF EXPERIMENTS Jenna Christina Haenggi
- Some Useful Distributions Definition 10.1. The population median is any value MED(Y ) such that
- A Note on Partitioning David J. Olive
- Response Transformations for Models with Additive David J. Olive
- Exam 2 will cover sections 1.1-1.5, 2.1-2.9, 3.1, 3.2, 4.1 and 4.2 through Factorization theorem (through p. 115), with emphasis on sections 2.6, 2.7, 2.9. 3.1, 3.2, 4.1 and 4.2
- Orthogonal Designs Orthogonal designs for factors with two levels can be fit using least squares.
- Course Announcement Fall 2008, MATH 583: Advanced Topics in Statistics: ANOVA: Design and Analysis of Experiments
- Final Review: the Final is on Monday, May 8 12:50-2:50 (here). The final is cumulative but there is more emphasis on the material in Exam 3 and on
- CMCD Applications 11.1 DD Plots
- REGRESSION AND ANOVA UNDER HETEROGENEITY Elmer Agudelo Rodriguez
- Poisson Regression If the response variable Y is a count, then the Poisson regression model is
- Robust Multivariate Location and Dispersion David J. Olive and Douglas M. Hawkins
- Practical High Breakdown Regression David J. Olive and Douglas M. Hawkins
- The Number of Samples for Resampling Algorithms David J. Olive
- Response Plots and Related Plots for Regression David Olive
- Plots for Binomial and Poisson Regression David J. Olive
- Applications of a Robust Dispersion Estimator Jianfeng Zhang and David J. Olive
- Resistant Dimension Reduction Jing Chang and David J. Olive
- Inference for the Pareto, half normal and related distributions
- Robustifying Robust Estimators David J. Olive and Douglas M. Hawkins
- The Breakdown of Breakdown David J. Olive and Douglas M. Hawkins
- Behavior of elemental sets in regression David J. Olive and Douglas M. Hawkins
- A Resistant Estimator of Multivariate Location and David J. Olive
- Inconsistency of Resampling Algorithms for High Breakdown Regression Estimators and a New
- High Breakdown Analogs of the Trimmed Mean David J. Olive
- Applications and Algorithms for Least Trimmed Sum of Absolute Deviations Regression
- Introduction All models are wrong, but some are useful.
- Truncated Distributions This chapter presents a simulation study of several of the confidence intervals
- Multiple Linear Regression In the multiple linear regression model,
- Robust and Resistant 7.1 High Breakdown Estimators
- Resistance and Equivariance 9.1 Resistance of Algorithm Estimators
- Multivariate Models Definition 10.1. An important multivariate location and dispersion model
- Generalized Linear Models 13.1 Introduction
- Stuff for Students 14.1 Tips for Doing Research
- Introduction All models are wrong, but some are useful.
- K Way ANOVA 6.1 Two Way ANOVA
- More on Experimental Designs The one and two way Anova designs, completely randomized block design
- Logistic Regression Multiple linear regression is used when the response variable is quantitative,
- Theory for Linear Models Theory for linear models is used to show that linear models have good sta-
- Survival Analysis In the analysis of "time to event" data, there are n individuals and the time
- Stuff for Students 17.1 R/Splus and Arc
- Does the MLE Maximize the Likelihood? David Olive
- Course Announcement MATH 580: Statistical Theory, Spring 2008, 3:00-3:50 MWF This course will cover the theoretical background for statistical procedures in the
- Exponential Families This handout expands on section 3.4 in (Casella and Berger, 2002) CB who define a
- Course Announcement Fall 2011, MATH 490: Topics in Mathematics: Robust Statistics
- A Course in Statistical Theory David J. Olive
- Probability and Expectations 1.1 Probability
- Multivariate Distributions and Transformations
- Point Estimation 5.1 Maximum Likelihood Estimators
- BIBLIOGRAPHY 413 1. Abuhassan, H. (2007), Some Transformed Distributions, Ph.D. Thesis,
- Math 250 Exam 1 review. Thursday Feb. 5. Bring a TI30 calculator but NO NOTES. Emphasis on sections 5.3, 5.5, 6.1, 6.2, 6.3, 3.7; HW1-5; Q1-4. Know for trig
- Math 250 Exam 3 review. Thursday April 9. Bring a TI30 calculator but NO NOTES. Emphasis on sections 5.5, 6.1, 6.2, 6.3, 3.7, 6.6, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,
- Exam 2 review. Thursday, March 10. Bring a TI 30 calculator but NO NOTES. Emphasis is on sections 1.3, 1.4, 1.5, 1.6, 2.1, 2.2, 2.3, 3.1, 3.2, 3.3; HW2-12; Q1-11.
- Exam 3 review, Thursday, April 21. TI30 calculator but no notes. Emphasis is sections 1.3, 1.4, 1.5, 1.6, 2.1, 2.2, 2.3, 3.1, 3.2, 3.3, 3.5, 3.6, 4.1, 4.2, 4.3, 4.4, 4.5, 4.7,
- Exam 4 review. Thursday, May 5. Emphasis is on section 2.1 (tangent line); 2.3, 3.3, 3.5, 3.6 (basic derivatives); 2.5 (chain rule); 2.6 (implicit differentiation); 2.7 (related
- Final review. The final is Tuesday Dec. 9 10:10 -12:10 in 219 EGRA (engineering) building. From reviews 1), 2), 3) and 4), know how to do all of the problems
- The following list of references for math and statistics texts may be useful. High Quality Online Texts and Notes
- March 2011: The Society of Actuaries (SOA, see www.soa.org) and Casu-alty Actuarial Society (CAS, see www.casact.org) give exams and accredit the
- Some Suggestions for Teaching the Sciences To be blunt, many of us are lousy teachers, and our efforts to improve are feeble. So
- Sept. 2006: To get a Master's degree in Probability and Statistics at SIU, the following theoretical and applied courses should be taken or have been taken as an
- Aug. 2010: The SIU Math department occasionally produces a Ph.D. in Statistics, but many universities offer an easier path to a PhD in Statistics. A good Ph.D.
- Course Announcement MATH 485: Applied Statistical Methods: Fall 2010 MWF 11:00-11:50 WHAM 0317
- Exam 1 is Wed. Sept. 22. You are allowed 3? sheets of notes and a calculator. The exam covers survey sampling.
- Exam 2 is Wednesday March 8. 4 sheets of notes The material for categorical data follows Agresti closely.
- Large Sample Theory 8.1 The CLT, Delta Method and an Expo-
- Elemental Fits are Dense David J. Olive
- Regression Diagnostics Using one or a few numerical summaries to characterize the relationship
- Applications of Robust Distances for Regression David J. Olive
- Testing Statistical Hypotheses A hypothesis is a statement about a population parameter , and in hypoth-
- BINOMIAL CONFIDENCE INTERVALS AND DIAGNOSTICS FOR BINOMIAL REGRESSION
- Robust Regression Algorithms Recall from Chapter 7 that high breakdown regression estimators such as
- Visualizing 1D Regression David J. Olive
- A Simple Confidence Interval for the Median David J. Olive
- Using Exponential Families in an Inference Course David J. Olive
- EXAM3, FINAL REVIEW (and a review for some of the QUAL problems): No notes will be allowed, but you may bring a calculator. Memorize the pmf or pdf f,
- Plots for Generalized Additive Models David J. Olive
- Variable Selection for 1D Regression Models David J. Olive
- Response Plots for Experimental Design David J. Olive
- Some Useful Distributions The two stage trimmed means of Chapter 2 are asymptotically equivalent to
- High Breakdown Multivariate Estimators David J. Olive
- Block Designs Definition 7.1. A block is a group of k similar or homogenous units.
- UMVUEs and the FCRLB Warning: UMVUE theory is rarely used in practice unless the UMVUE Un
- The Location Model 2.1 Four Essential Statistics
- Exam 1 on Wed. Feb. 13 will cover sections 1.11.6 and 2.12.5. Memorization tip: On the left hand side of a piece of paper, write key words like
- 1D Regression ... estimates of the linear regression coefficients are relevant to the linear
- One Way ANOVA 5.1 Introduction
- Multivariate Models Definition 14.1. An important multivariate location and dispersion model
- Robust Estimators for Transformed Location Scale David J. Olive
- Prediction Intervals in the Presence of Outliers David J. Olive
- Multiple Linear Regression 2.1 The MLR Model
- RESISTANT DIMENSION REDUCTION Master of Science in Mathematics, Tennessee Technological University, 2002
- Building an MLR Model Building a multiple linear regression (MLR) model from data is one of the
- Generalized Linear Models 12.1 Introduction
- Prediction Intervals for Regression Models David J. Olive
- A Note on Visualizing Response Transformations in R. Dennis Cook
- Sufficient Statistics 4.1 Statistics and Sampling Distributions
- BIBLIOGRAPHY 593 1. Abraham, B., and Ledolter, J. (2006), Introduction to Regression Mod-
- Response Plots for Linear Models David J. Olive
- Exam 1 review. Thursday Feb. 10. Bring a TI-30 calculator but NO NOTES. Emphasis on sections 1.3, 1.4, 1.5, 1.6, 2.1, 2.2, 2.3; HW2-5; Q1-4. Know for trig
- Graphical Aids for Regression David Olive
- COMMENTS ON BREAKDOWN David J. Olive
- APPLICATIONS OF A ROBUST DISPERSION ESTIMATOR Jianfeng Zhang
- Association for the Chi-square Test David J. Olive
