Ivor Cribben

Associate Professor, Department of Accounting and Business Analytics

Contact

Associate Professor, Department of Accounting and Business Analytics
Email
cribben@ualberta.ca
Phone
(780) 248-1930
Address
4-30G Business Building
11203 Saskatchewan Drive NW
Edmonton AB
T6G 2R6

Overview

Research

Research Interests

  • Time Series Analysis
  • Statistical Methods in fMRI
  • Methods for High Dimensional Data
  • Graphical Models
  • Non-Parametric Statistics
  • Extreme Value Theory
  • Neuroeconomics

Teaching

Teaching Interests

  • Forecasting for Planners and Managers
  • Applied Business Analysis of Time Series and Panel Data
  • Multivariate Data Analysis

Announcements

A Spotlight on Research at the Alberta School of Business

How is neuro statistics linked with decision-making?

My findings tell us...

  • The brain's functionality and dynamic integration can be statistically analyzed by looking at networks.
  • Statistical analysis of fMRI scans shows that people with gambling disorders have unique brain networks as they are making financial decisions.
  • The comparison of networks can be further applied to a broad spectrum of brain disorders such as dyslexia, cerebral palsy and Alzheimer's. 

Read more about this research...

Courses

MGTSC 405 - Forecasting for Planners and Managers

This course is concerned with methods used to predict the uncertain nature of business trends in an effort to help managers make better decisions and plans. Such efforts often involve the study of historical data and manipulation of these data to search for patterns that can be effectively extrapolated to produce forecasts. This is a business statistics course that covers all aspects of business forecasting where the emphasis is on intuitive concepts and applications. Topics covered include the family of exponential smoothing methods, decomposition methods, dynamic regression methods, Box-Jenkins methods and judgmental forecasting methods (e.g. the Delphi method). Because forecasting is best taught through practice, the course contains numerous real, relevant, business oriented case studies and examples that students can use to practice the application of concepts. Prerequisites: MGTSC 312, MGTSC 352 or OM 352.

Fall Term 2020
MGTSC 488 - Selected Topics in Management Science

Normally restricted to third- and fourth- year Business students. Prerequisites: MGTSC 312 or consent of Department. Additional prerequisites may be required.

Fall Term 2020
MGTSC 705 - Multivariate Data Analysis I

An overview of multivariate data analysis normally taken by students in the first year of the Business PhD program. Designed to bring students to the point where they are comfortable with commonly used data analysis techniques available in most statistical software packages. Students are expected to complete exercises in data analysis and in solving proofs of the major results. Topics will include univariate analysis, bivariate analysis, multiple linear regression, and analysis of variance. It is expected that students have as background at least one semester of calculus, one semester of linear algebra, and two semesters introduction to probability, probability distributions and statistical inference. Prerequisite: Registration in Business PhD Program or written permission of instructor. Approval of the Business PhD Program Director is also required for non-PhD students.

Fall Term 2020
OM 710 - Individual Research

Fall Term 2020

Browse more courses taught by Ivor Cribben

Publications

Estimating whole‐brain dynamics by using spectral clustering
Author(s): Cribben, I., Yu, Y.
Publication Date: 2017
Publication: Journal of the Royal Statistical Society: Series C (Applied Statistics)
Volume: 66
Issue: 3
Page Numbers: 607-627
External Link: https://arxiv.org/pdf/1509.03730.pdf
Discussion of “Should we sample a time series more frequently? Decision support via multirate spectrum estimation” by Nason, Powell, Elliott and Smith
Author(s): Yu, Y., Cribben, I.
Publication Date: 2016
Publication: Journal of the Royal Statistical Society: Series A (Statistics in Society)
Volume: 180
Issue: 2
Page Numbers: 384-386
External Link: http://onlinelibrary.wiley.com/doi/10.1111/rssa.12210/full
extremogram: estimating extreme value dependence
Author(s): Frolova, N, Cribben, I.
Publication Date: 2016
External Link: https://cran.r-project.org/web/packages/extremogram/index.html
Detecting functional connectivity change points for single-subject fMRI data
Author(s): Cribben, I., Wager, T.D., Lindquist, M.A.
Publication Date: 2013
Publication: Frontiers in Computational Neuroscience
Volume: 7:143
External Link: http://www.frontiersin.org/Journal/10.3389/fncom.2013.00143/abstract
Dynamic Connectivity Regression: Determining state-related changes in brain connectivity
Author(s): Cribben, I., Haraldsdottir, R., Atlas, L., Wager, T.D., Lindquist, M.A.
Publication Date: 2012
Publication: NeuroImage
Volume: 61
Page Numbers: 907 - 920
External Link: http://www.sciencedirect.com/science/article/pii/S1053811912003515
Towards Estimating Extremal Serial Dependence via the Bootstrapped Extremogram
Author(s): Davis, R.A., Mikosch, T., Cribben, I
Publication Date: 2012
Publication: Journal of Econometrics
Volume: 170
Page Numbers: 142 - 152
External Link: http://dx.doi.org/10.1016/j.jeconom.2012.04.003