- Ivan Mizera and Christine H. Muller 1. Introduction
- STAT 578: Lecture 14 More inference
- Solutions to HW4 1. (a) One needs a bit of detective skills to figure out what the additional data mean; but from the paired presentation one
- STAT 578: Lecture 10 Regularization
- STAT 578: Lecture 11 Inference in linear model
- Continuity of halfspace depth contours and maximum depth estimators: diagnostics of
- Location-Scale Depth Ivan Mizera and Christine H. M uller
- Sample heterogeneity and the asymptotics of Mestimators
- STAT 441: Lecture 6 Factor analysis
- STAT 368: Lecture 12 ANOVA with interactions
- The alter egos of the regularized maximum likelihood density estimators: deregularized maximum-entropy,
- STAT 368: Lecture 8 Analysis of paired data
- STAT 441: Lecture 11 Linear discriminant analysis
- STAT 441: Lecture 21 Pretty much like univariate ANOVA
- STAT 368: Lecture 9 Principles of ANOVA
- STAT 368: Lecture 16 Fractional factorial designs
- STAT 578, Assignment 5. Due Wednesday, October 17, 11:30 As announced in class, there is no computational part this time, and the homework amounts to solving
- STAT 368: Lecture 3 Comparing two groups I
- STAT 578, Lecture 6 Hill Climbing
- Solutions to HW5 1. We first do (c). Remember that after reading the data we should make them factors, otherwise we have it badly wrong, as
- STAT 441: Lecture 13 Classification via regression II
- STAT 578: Lecture 8 Regressions with nonstandard responses
- An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics
- STAT 441: Lecture 19 Multivariate analysis
- DENSITY ESTIMATION BY TOTAL VARIATION REGULARIZATION
- Breakdown points of Cauchy regression-scale estimators Ivan Mizera
- WHAT DO KERNEL DENSITY ESTIMATORS OPTIMIZE? ROGER KOENKER, IVAN MIZERA, AND JUNGMO YOON
- STAT 368: Lecture 10 Randomized blocks as two-way layout
- STAT 368: Lecture 7 Fighting variability
- STAT 578, Assignment 1. Due Monday, September 20, 12:00 A. Computational part
- Accepted by the Annals of Statistics QUASI-CONCAVE DENSITY ESTIMATION
- STAT 441: Lecture 14 Classification via regression III
- STAT 441: Lecture 1 Seeing the data
- The in uence of the design on the breakdown point of ` 1 -type
- STAT 578: Lecture 1 What is regression, after all?
- STAT 368: Lecture 14 Graphical analyses of factorial designs
- STAT 368: Lecture 13 Factorial designs at two levels
- STAT 441: Lecture 7 Multidimensional scaling
- STAT 368: Lecture 20 Linear model
- IMS Collections Festschrift
- On depth and deep points: a calculus Ivan Mizera
- R Installation and Administration Version 2.12.1 (2010-12-16)
- STAT 368: Lecture 2 Reading: Chapter 2 -this is considered a prerequisite, and we will return to it only
- STAT 368: Lecture 1 The Obvious
- STAT 368: Lecture 5 Comparing two groups III
- STAT 368: Lecture 11 Latin squares and all that
- STAT 368: Lecture 17 Factors revisited
- STAT 368: Lecture 18 ANOVA revisited: random factors
- SOLUTIONS TO HW1 In the usual spirit, there are always several ways to do or graph it (not the varied style of graphs!), so the following solutions
- SOLUTIONS TO HW2 > branda <-c(2,4,2,1,9,9,2,2)
- Solutions to HW3 > Coat <-data.frame(reading=c(4.0,4.8,4.0,4.8,5.0,4.8,5.0,5.2,5.6,4.6,4.6,5.0),
- Midterm review sheet Prerequisites: all of Chapter 2 is supposed, except perhaps for 2.14 (but
- STAT 578: Lecture 2 General regression formalism
- STAT 578: Lecture 4 Why least squares -and why not
- STAT 578, Lecture 5 Models and parametrizations
- STAT 578, Lecture 7 Robust Regression
- STAT 578: Lecture 9 Splines as flexible regression curves
- STAT 578: Lecture 12 Model selection and tuning
- STAT 578: Lecture 13 Nonlinear regression revisited
- STAT 578, Assignment 2. Due Friday, October 1, 11:30 A. Computational part
- STAT 578, Assignment 6. Due Friday, December 3, 11:30 A. Computational part
- Using S for the Unwilling Patrick Burns
- STAT 441: Lecture 3 A quick glimpse on
- STAT 441: Lecture 4 Seeing the data (projections)
- STAT 441: Lecture 5 Principal components
- STAT 441: Lecture 8 Cluster analysis
- STAT 441: Lecture 10 Classification
- STAT 441: Lecture 12 Classification via regression I
- STAT 441: Lecture 15 Classification trees
- STAT 441: Lecture 16 Classification: finale
- STAT 441: Lecture 17 Canonical correlations
- STAT 441: Lecture 20 Inference based on
- STAT 441 Midterm Review Questions and Sample Problems Coverage: Lectures 1-8 + everything relevant to them from the textbook + Chapter 11 + some elementary
- SONG RECITAL 9 September 2003
- STAT 441: Lecture 2 Some features of S
- STAT 368: Lecture 4 Comparing two groups II
- STAT 368, Midterm Warm-Up Note: questions on the real exam will be different. You may use a simple calculator. The answers and explanations should be correct, but short. You can
- STAT 368: Lecture 15 Blocking in factorial designs
- PENALIZED TRIOGRAMS: TOTAL VARIATION REGULARIZATION FOR BIVARIATE
- STAT 441: Lecture 18 Multivariate analysis
- Tatra Mt. Math. Publ. ?? (????), 001009 tmMathematical Publications
- Breakdown points of Cauchy regression-scale estimators Ivan Mizera
- On depth and deep points: a calculus Ivan Mizera
- STAT 441: Lecture 9 Classification (supervised)
- Continuity of halfspace depth contours and maximum depth estimators: diagnostics of
- STAT 368: Lecture 19 Split-plot experiments
- STAT 368: Lecture 6 Randomization
- STAT 578, Assignment 3. Due Wednesday, October 13, 11:30 A. Computational part
- STAT 578: Lecture 3 Least squares
- STAT 441 Final Review Questions Note that there are some new questions also for the topics already covered by the midterm ones.
