Advanced statistical modeling: Introduction to Non-linear analysis

Date & Time

Thursday, 23 February 2023
10:00 to 12:00

Location

Online

Offered by

Graduate Pathways to Success and Applied Statistics and Data Science Group (ASDa)

Registration Opens

Monday, 13 February 2023 - 9:00am
 
 

In this webinar we will discuss the foundational concepts behind non-linear modeling, when should these models be applied and how to implement them correctly. We will show how this complex methodology can be applied in a correlated data example. While complete R code is provided for this analysis, the focus of this webinar will be on the statistical methodologies.

You will learn how to:

  • Choose and fit non-linear model to highly non-linear data
  • Understand the basic concepts of the mathematical models for non-linear data
  • Explore the parameter space of the mathematical models so that appropriate starting points are used as inputs in the model fitting algorithms. This will involve the introduction of perspective and contour plots (this tool is crucial to determining the starting points of the optimizer for non-linear models, so it could be very helpful but will contribute to too much content if we introduce the splines, so either this or splines topics will be covered here)
  • Interpret the results of non-linear models
  • Introduce splines basics to allow for flexible fitting of nonlinear data at the expense of interpretability
More on this series

This webinar series was made possible via joint collaboration between Faculty of Science (FOS), Applied Statistics and Data Science Group (ASDa) and Graduate Pathways for Success Program (GPS), funded by the UBC Science Strategic Innovation Fund.

Statistics is a scientific discipline that enables reaching meaningful conclusions from data. To produce reliable results, you need to justify the choice of the applied statistical methods and models as well as validate the underlying assumptions.

This series of two 2-hour webinars provide introductions to the foundational concepts of advanced statistical methodologies in non-linear models and machine learning algorithms. We will discuss different statistical models and machine learning methods, their appropriate application and how to interpret the results obtained from them. The aim is to equip the attendees with a deeper understanding of the key concepts of statistical and machine learning methodology, rather than solving specific project problems. While R code for hands-on guidance is provided the focus of this series will be on the methodological aspects.

Each webinar is a self-contained introduction to different advanced statistical concepts, but as topics become increasingly complex with each consecutive webinar, some aspects will be built on concepts taught in the previous sessions including the webinar series on foundational concepts. Hence, there is benefit in attending all the webinars.

If you are a graduate student and have questions about your specific project, please see the SOS Program to book a one-hour free statistical consultation. 

ABOUT ASDA

The Applied Statistics and Data Science Group (ASDa) in the UBC Department of Statistics provides statistical consulting services and participates in collaborative research. ASDa expertise includes problem formulation, translation of research questions into testable statistical hypotheses, design of experiments and sampling plans for surveys, the choice and explanation of statistical methodology, statistical computing and graphics, the interpretation of findings and more. ASDa also plays an active role in continuing education on and off the UBC campus, giving seminars, webinars, hands-on workshops and courses on statistical concepts and methodologies to various departments, research groups and at teaching hospitals.

Facilitator

Biljana Jonoska Stojkova, PhD, is a Senior Statistical Consultant with ASDa, participating in collaborative research, providing statistical consulting services and tailored education on statistical concepts and analytics tools on and off campus, as well as participating in collaborative research. She completed her PhD in Statistics at SFU in 2017, where she focused on developing Bayesian algorithms and methods for multi-modal posterior spaces, which were applied to differential equation models, mixture Gaussian models, epidemiological and ecological models. In the previous roles she has gained experience with probabilistic models to determine different patterns of user behavior from chat messages, with development of relational databases and with machine learning algorithms such as supervised and unsupervised learning. In her consulting role Biljana continues to strengthen her skills in problem formulation, study design, grant proposal development, analysis and implementation, and continued education of non-statisticians on and off the UBC campus, giving webinars, workshops and courses on statistical concepts and methodology to various departments, research groups and at teaching hospitals.

Registration Information

General registration opens on Monday, February 13th at 9:00 AM.

Priority will be given to UBC graduate students registered in the current academic session. After registering, you will receive confirmation and additional event details within 2 - 3 business days at the e-mail associated with your community.grad.ubc.ca account. If you experience any difficulty using the online registration tool, please e-mail us at graduate.pathways@ubc.ca.

Please email us if you are registered and no longer able to attend this event.

Accessibility

If you have a disability or medical condition that may affect your full participation in the event, please email graduate.pathways@ubc.ca, 604-827-4578, well in advance of the event.