Comet Calendar

Statistics Seminar by Ashok Chaurasia (Waterloo)

Friday, December 9, 2022 at 11:00am to 12:00pm

Virtual Event

Model-modified BIC as a competitor of BIC variants for model selection in regression and order selection in time series

Model Selection is a ubiquitous task in the model building process that seeks a parsimonious model to explain patterns in data. Bayesian Information Criteria (BIC) is one such tool that is used in applications for its property of consistency, where this term is traditionally synonymous with asymptotic consistency. This article introduces the concept of exact consistency in model selection (which places an upper bound on the probability of selecting under-fitted models) to elucidate finite-sample properties of asymptotically consistent criteria. A new class of criteria gBIC is proposed with conditions for candidate model priors that guarantee exact and asymptotic consistency. gBIC provides a theoretical means to assess the consistency of existing BIC variants by simply checking functional properties of their respective penalty components. It is shown that gBIC includes sample size adjusted BIC (SABIC) as a special case, and improves on BIC with the proposed model-modified BIC (mBIC). In comparison to BIC, mBIC guarantees higher success rates for selecting models that match or contain the data generating model at any sample size for linear regression and time series. Lastly, mBIC can easily be adopted in linear regression and time series via simple algebraic adjustments to BIC/SABIC values.

Persons with disabilities may submit a request for accommodations to participate in this event at UT Dallas' ADA website. You may also call (972) 883-5331 for assistance or send an email to All requests should be received no later than 10 business days prior to the event.

Event Type

Lectures & Workshops

Target Audience

Undergraduate Students, Faculty & Staff, Graduate Students


Research, Science & Technology


statistics, data science, stat.fall2022, math

Natural Sciences & Mathematics
Contact Information
Qiwei Li
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