STATISTICS 525

Advanced Topics in Data Analysis

Fall 2005

Instructor:

Professor Jiayang Sun, Yost 326, 368-0630, jsun@case.edu
Office hours: 4-5 on Tuesday and Thursday.

TA:

Rongfang Gu, Yost 329, 368-0472, rongfang.gu@case.edu
Office hours: 11:30-1:00pm Tuesday and Thursday, 8:45am - 9:45am Thursday @ Yost 234

Classes:

1:15-2:30 pm on Tuesday and Thursday at Yost 327.

Course Outline:

The course this year concentrates on modern nonparametric techniques in statistics, especially for density estimation, regression and applications. Topics include histograms, kernel estimates, splines, orthogonal series estimates and wavelets, local polynomial regression and local likelihood methods, neural network and projection pursuit. If time permits, there will be an introduction to other goodies for data mining. The emphases will be on practical methods and data applications.

Prerequisite:

Stat 425 and some knowledge about Splus.

Recommended Text Book:

. Wasserman (2005). All of Nonparametrics. Springer.

. Simonoff (1996). Smoothing Methods in Statistics. Springer. (Reserved in the Kevin-Smith lib.)

Other References:

. Lange (1999). Numerical Analysis for Statisticians. Springer. (Reserved in the Kevin-Smith lib.)
. Efromovich (1999). Nonparametric Curve Estimation. Springer. (Reserved in the Kevin-Smith lib.)
. Hastie, Tibshirani & Friedman (2001), The Elements of Statistical Learning: data mining, inference and prediction. Springer. (Reserved in the Kevin-Smith lib.)
. McLachlan and Basford (1988). Mixture Models: inference and applications to clustering. Marcel Dekker.

. Heiberger and Holland (2004). Statistical Analysis and Data Display; An Intermediate Course with Examples in S-PLUS, R, and SAS. Springer Texts in Statistics, ISBN: 0-387-40270-5. (Reserved in the Kevin-Smith lib.)

Grades:

The assessment will be based on homework assignments (40%), an oral presentation (15%), and a final project (45%).