Thomas M. Loughin


Department of Statistics and Actuarial Science

Simon Fraser University




Statistics and Actuarial Science

Simon Fraser University

8888 University Drive

Burnaby, BC V5A 1S6





SSC K-10549






E-MAIL: tloughin "at" sfu "dot" ca

 What the heck happened in the middle???

I actively support principles of equity, diversity, and inclusiveness. I strongly encourage outstanding statistics undergraduates of all personal backgrounds to apply for graduate study in the department and with me. I believe that diversity of student backgrounds enriches my lab, and I welcome the opportunity to work and engage with all students.

My book, Analysis of Categorical Data with R, is now available!  The book’s website is here.  Order a copy directly from the publisher, CRC Press, or from fine merchants around the world.

My Duties include:


·       I am interested in developing Statistical Methodologies in a wide range of areas. Recent work includes:

·       Statistical learning, including regression trees and tree-based ensembles .  Regression trees, as they are currently used---both alone and in ensembles---are very limited tools. With my graduate students we are developing tree and ensemble algorithms that adapt better to unexpected data structures and are more easily tuned than usual methods.

·       Modeling categorical data, particularly the analysis of multiple response categorical data. This problem arises often in surveys where respondents are told to “mark all that apply”. Surprisingly, until recently, there were no good, established methods for modeling data of this type. Chris Bilder and I have been working on a class of models that are flexible yet easy to interpret. See Chris’s website.

·       Multi-Label Classification.  Multi-label classification (MLC) deals with the question of how to best classify objects on which a vector of binary classes is observed. Independent classification of each label ignores potentially valuable information on the joint distribution of the binary labels. This problem lies at the intersection of my work on statistical learning and modeling "choose all that apply" questions.

·       Analysis of unreplicated experiments.  When factorial experiments are performed without replication, the analysis of factors that affect the mean is tricky. Even trickier is the analysis of factors that affect the variance. In collaboration with students, I have developed several methods for improving inferences from unreplicated factorials.

·       Design and analysis of long-term experiments. When field trials and certain other experiments are run over a long time, they are subject to random effects due to years. Most experimental designs and analysis methods address this problem very poorly. With colleagues, we have developed improved designs and are applying them to actual long-term experiemnts in fisheries research,

·       Collaborative Work with researchers in various areas.  Recent areas of collaboration include Pediatric Medicine; Cardiovascular Physiology; Wildlife Ecology and Biology; Interactive Arts and Technology; Laboratory Testing; and others.

·       A detailed CV is available here (pdf format) .



·       I am available for statistical consulting and collaboration for SFU researchers as well as external people and organizations. My internal consulting is mainly limited to collaborations where publication is expected and a statistical collaborator would be helpful. Ian Bercovitz at SFU Statistical Consulting Service is excellent and can handle other problems.   Read here to find out more about the SFU Statistical Consulting Service.

·       The best time to reach out for statistical consulting is in the planning phase of the study. Statistical consultation early can help to avert a disaster much more easily than we can help to recover from one.

·       Don’t make these mistakes!


Fall 2020:

·        STAT 452: Statistical Learning and Prediction / STAT 652: Intro to Statistical Learning

·        STAT 852: Modern Methods in Applied Statistics

My teaching history includes a wide range of courses:

·        Graduate Level Statistics: Modern Methods in Applied Statistics (Statistical Learning), Bootstrapping, Linear Models and Messy Data, Multiple Testing and Multiple Comparisons, Lifetime Data Analysis

·        Upper-Level Applications Courses: Statistical Learning, Categorical Data Analysis, Design of Experiments, Applied Linear Models

·        Service Courses: Intro to Stat, Chance and Data Analysis, ANOVA, Regression, Intermediate Probability and Statistics


·       The SAS/IML program to perform the Loughin and Noble (1997) permutation test for unreplicated factorials is here


Details regarding my education, background, experience, and publications can be found on my
Expanded CV (pdf)

Department of Statistics Faculty

SFU Department of Statistics and Actuarial Science Homepage

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Last Update: 29 Aug 2020