Short Course Descriptions

We plan to offer four short courses on July 14.

PLS Latent Variables Soft Modelling (9:00 am – Noon)

Vincenzo Esposito Vinzi
Dipartimento di Matematica e Statistica
UniversitÓ degli Studi di Napoli "Federico II"
Via Cintia 26 - Complesso Monte Sant'Angelo - 80126 Napoli - Italy

Abstract. This shortcourse has the purpose to provide the audience with a general presentation of LISREL and a more detailed presentation of PLS Path Modelling for the study of causal relationships between blocks of variables by referring to methods, software and applications. Namely, the following objectives are pursued:

Introduction to Microarrays and Clustering (9:00 am – Noon)

Bill Shannon
Washington University in St. Louis School of Medicine

Abstract. This three hour short course is divided into two sections. The first half will be an overview of the basic biology and microarray technology platform. This will include differential gene expression, microarray platforms, and image analysis for capturing the data. The second half will focus on statistical clustering methods for analyzing microarray data.

This course is aimed at new biomedical investigators who want a basic overview of the statistical issues related to microarrays, and the data analyst wanting a basic overview of the biology behind this methodology.

The course will be based on the following two papers:

DNA Microarray Experiments: Biological and Technological Aspects. Danh V. Nguyen, A. Bulak Arpat, Naisyin Wang, and Raymond J. Carroll, Biometrics, 58(4), pp. 701-717, (2002).

Analysing microarray data using cluster analysis (Review) William Shannon, Robert Culverhouse and Jill Duncan, Pharmacogenomics 4(1), 41- 52 (2003)

Axiomatics in Bioconsensus (1:00 pm – 4:00 pm)

William H. E. Day
Port Maitland, Nova Scotia, Canada

Latent Class Models for Clustering and Classification (1:00 pm – 4:00 pm)

Jay Magidson,
Statistical Innovations
Belmont MA.


Tony Babinec,
AB Analytics
Chicago IL.

Abstract. The various uses of latent class and finite mixture models for clustering and classification are growing rapidly because of:

  1. the lack of restrictive assumptions underlying the general model,

  2. major developments in maximum likelihood estimation of these models, and

  3. availability of model parameters to use for classifying new cases.

This short course introduces the latent class (LC) and finite mixture approach to clustering and focuses on three important LC models -- cluster, factor, and regression -- for combinations of nominal, ordinal, and/or continuous variables. Applications are taken from the fields of marketing research and the biomedical sciences. Topics include (1) relationship to and improvements over K-means clustering, (2) use of simultaneous cluster and regression/discriminant/choice analyses as an improvement over the traditional tandem cluster-regression analyses, and (3) use of covariates and an extended CHAID algorithm to describe the resulting latent class segments. We will provide extensive references and a description of the major latent class modeling software available. The Latent GOLD program and new Latent GOLD Choice module will be used for illustration.

A list of references, technical articles and a demonstration version of the Latent GOLD program can be obtained at