Ivor Cribben
Contact
Professor, Alberta School of Business - Department of Accounting and Business Analytics
- mylastname@ualberta.ca
- Phone
- (780) 248-1930
- Address
-
4-20D Business Building
11203 Saskatchewan Drive NWEdmonton ABT6G 2R6
Overview
About
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.
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.
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.
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.
MMA 600 - Coding Bootcamp
Two-Week Kick Start Bootcamp: Embark on a seamless learning journey as students engage in a well-rounded experience to master two essential programming languages - Python and R. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.
Featured Publications
Xiong, X., Cribben, I.
The American Statistician. 2022 October; 10.1080/00031305.2022.2131625
Xiong, X., Cribben, I.
Journal of Computational and Graphical Statistics. 2022 September; 10.1080/10618600.2022.2127738
Ondrus, M., Cribben, I.
arXiv preprint arXiv:2207.02986.. 2022 July; 10.48550/arXiv.2207.02986
Han, L., Cribben, I., Trueck, S.
arXiv preprint arXiv:2202.09970.. 2022 February; 10.48550/arXiv.2202.09970
Anastasiou, A., Cribben, I., & Fryzlewicz, P.
Medical Image Analysis. 2021 September; 75 10.1016/j.media.2021.102252
Zhang, W., Cribben, I., Petrone, S., Guindani, M.
arXiv preprint arXiv:2106.14083. 2021 June; 10.48550/arXiv.2106.14083
Xiong, X., Cribben, I.
R package version. 2021 March; 1
Ondrus, M., Cribben, I.
R package version. 2021 February; 2
Ding, L., Cribben, I., Ingolfsson, A., Tran, M.
arxiv. 2021 February;
Anastasiou, A., Cribben, I., & Fryzlewicz, P.
R package version. 2021 January; 1
Ofori-Boateng, D., Gel, Y., Cribben, I.
Journal of Computational and Graphical Statistics . 2020 October; 30 (3):756-767
Xu, M., Reiss, P.T., Cribben, I.
Biometrics. 2020 April; 77 (1):258-270 10.1111/biom.13287
Brown, M.R.G., Agyapong, V., Greenshaw, A.J., Cribben, I.
BMC Psychiatry. 2018 December; 19 (1):1-11
Cribben, I.
Applied Stochastic Models in Business and Industry. 2018 August; 35 (2):299-320
Hart, B., Cribben, I., Fiecas, M.
NeuroImage. 2018 May; 178
Rezaei, M., Cribben, I., Samorani, M.
Annals of Operations Research. 2018 April;
Zhu, Y., Cribben, I.
Brain Connectivity. 2018 April; 8 (3):139-165
Cribben, I., Yu, Y.
Journal of the Royal Statistical Society: Series C (Applied Statistics). 2017 January; 66 (3):607-627
Frolova, N, Cribben, I.
2016 January;
Cribben, I., Wager, T.D., Lindquist, M.A.
Frontiers in Computational Neuroscience. 2013 January; 7:143
Cribben, I., Haraldsdottir, R., Atlas, L., Wager, T.D., Lindquist, M.A.
NeuroImage. 2012 January; 61
Davis, R.A., Mikosch, T., Cribben, I
Journal of Econometrics. 2012 January; 170