Ivor Cribben

Associate Professor, Alberta School of Business - Department of Accounting and Business Analytics

Contact

Associate Professor, Alberta School of Business - 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

About

Google Scholar


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
  • Business Analytics

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.

Winter Term 2022
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.

Winter Term 2022
MGTSC 645 - Introduction to Business Analytics

The merging of massive data-sets with analytical tools from Statistics, Computer Science, and Operations Research has created the emerging field of analytics. Methods are developing rapidly based on statistical platforms such as SAS and R, or more general purpose programming tools such as Python. This course will build on the basis from MGTSC 501 to provide an overview of Big Data and analytics, and develop programming and methodological skills to acquire, analyze, and present analysis. Prerequisite: MGTSC 501.

Winter Term 2022
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.

Winter Term 2022
OM 710 - Individual Research

Fall Term 2021

Browse more courses taught by Ivor Cribben

Publications

Beyond linear dynamic functional connectivity: a vine copula change point model
Author(s): Xiong, X., Cribben, I.
Publication Date: 4/26/2021
Publication: bioRxiv
External Link: https://www.biorxiv.org/content/10.1101/2021.04.25.441254v1
Factorized Binary Search: change point detection in the network structure of multivariate high-dimensional time series
Author(s): Ondrus, M., Old, E., Cribben, I.
Publication Date: 3/10/2021
Publication: arXiv:2103.06347
Page Numbers: 1-21
External Link: https://arxiv.org/abs/2103.06347
vccp: Vine copula change point detection in multivariate time series
Author(s): Xiong, X., Cribben, I.
Publication Date: 3/3/2021
Publication: R package version
Volume: 1
External Link: https://cran.r-project.org/web/packages/vccp/vccp.pdf
fabisearch: a change point detection method in the network structure of multivariate high-dimensional time series
Author(s): Ondrus, M., Cribben, I.
Publication Date: 2/24/2021
Publication: R package version
Volume: 2
External Link: https://cran.r-project.org/web/packages/fabisearch/fabisearch.pdf
Do NHL goalies get hot in the playoffs? A multilevel logistic regression analysis
Author(s): Ding, L., Cribben, I., Ingolfsson, A., Tran, M.
Publication Date: 2/19/2021
Publication: arxiv
Page Numbers: 1-19
External Link: https://arxiv.org/abs/2102.09689
package 'ccid'
Author(s): Anastasiou, A., Cribben, I., & Fryzlewicz, P.
Publication Date: 1/7/2021
Publication: R package version
Volume: 1
External Link: https://cran.r-project.org/web/packages/ccid/index.html
Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity
Author(s): Anastasiou, A., Cribben, I., & Fryzlewicz, P.
Publication Date: 12/20/2020
Publication: bioRxiv
Page Numbers: 1-52
External Link: https://www.biorxiv.org/content/biorxiv/early/2020/12/22/2020.12.20.423696.full.pdf
Nonparametric Anomaly Detection on Time Series of Graphs
Author(s): Ofori-Boateng, D., Gel, Y., Cribben, I.
Publication Date: 10/14/2020
Publication: Journal of Computational and Graphical Statistics
Page Numbers: 1-12
External Link: https://www.tandfonline.com/doi/full/10.1080/10618600.2020.1844214
Generalized reliability based on distances
Author(s): Xu, M., Reiss, P.T., Cribben, I.
Publication Date: 4/27/2020
Publication: Biometrics
Page Numbers: 1-13
External Link: https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13287
After the Fort McMurray wildfire there are significant increases in mental health symptoms in grade 7–12 students compared to controls
Author(s): Brown, M.R.G., Agyapong, V., Greenshaw, A.J., Cribben, I.
Publication Date: 12/27/2018
Publication: BMC Psychiatry
Volume: 19
Issue: 1
Page Numbers: 1-11
External Link: https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-018-2007-1
Change points in heavy‐tailed multivariate time series: Methods using precision matrices
Author(s): Cribben, I.
Publication Date: 8/16/2018
Publication: Applied Stochastic Models in Business and Industry
Volume: 35
Issue: 2
Page Numbers: 299-320
External Link: https://onlinelibrary.wiley.com/doi/full/10.1002/asmb.2373
A longitudinal model for functional connectivity networks using resting-state fMRI
Author(s): Hart, B., Cribben, I., Fiecas, M.
Publication Date: 5/30/2018
Publication: NeuroImage
Volume: 178
Page Numbers: 687-701
External Link: https://www.sciencedirect.com/science/article/pii/S105381191830497X
A clustering-based feature selection method for automatically generated relational attributes
Author(s): Rezaei, M., Cribben, I., Samorani, M.
Publication Date: 4/5/2018
Publication: Annals of Operations Research
Page Numbers: 1-31
External Link: https://link.springer.com/article/10.1007/s10479-018-2830-2
Sparse graphical models for functional connectivity networks: best methods and the autocorrelation issue
Author(s): Zhu, Y., Cribben, I.
Publication Date: 4/1/2018
Publication: Brain Connectivity
Volume: 8
Issue: 3
Page Numbers: 139-165
External Link: https://www.liebertpub.com/doi/full/10.1089/brain.2017.0511
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://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12169
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