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X-WR-CALNAME:Institute of Statistical Research and Training
X-ORIGINAL-URL:https://isrt.ac.bd
X-WR-CALDESC:Events for Institute of Statistical Research and Training
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20251107T090000
DTEND;TZID=UTC:20251206T210000
DTSTAMP:20260116T211701
CREATED:20251013T063712Z
LAST-MODIFIED:20251023T053444Z
UID:8191-1762506000-1765054800@isrt.ac.bd
SUMMARY:Training on Stata for Applied Statistics and Data Science from November 07\,2025
DESCRIPTION:The upcoming Stata training program starts from November 07\, 2025\nRegistration Open!\nFollow the website for details: https://isrt.ac.bd/training/stata/\n 
URL:https://isrt.ac.bd/event/stata-for-applied-statistics-and-data-science-2/
LOCATION:ISRT\, ISRT\, Deltin 7 Aviator গেম টাকা ইনকাম of Deltin 7 bangladesh\, Deltin 7 bangladesh\, Bangladesh
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20251227T090000
DTEND;TZID=UTC:20251227T170000
DTSTAMP:20260116T211701
CREATED:20251223T081536Z
LAST-MODIFIED:20251223T081536Z
UID:8443-1766826000-1766854800@isrt.ac.bd
SUMMARY:pre-icasds2025 conference workshops
DESCRIPTION:More is given at https://icasds2025.isrt.ac.bd/home/pre-conference-workshops
URL:https://isrt.ac.bd/event/pre-icasds2025-conference-workshops/
CATEGORIES:workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251228
DTEND;VALUE=DATE:20251230
DTSTAMP:20260116T211701
CREATED:20240515T141523Z
LAST-MODIFIED:20250808T171935Z
UID:6653-1766880000-1767052799@isrt.ac.bd
SUMMARY:International Conference on Applied Statistics and Data Science (ICASDS) 2025 on December 28-29\, 2025 
DESCRIPTION:The International Conference on Applied Statistics and Data Science (ICASDS) is scheduled to take place on December 28-29\, 2025\, with a pre-conference workshop on December 27\, 2025. Further details and the conference website\, https://icasds2025.isrt.ac.bd will be available soon. \n 
URL:https://isrt.ac.bd/event/international-conference-on-applied-statistics-and-data-science-icasds-2025-on-december-27-29-2025/
CATEGORIES:conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260105T140000
DTEND;TZID=UTC:20260105T150000
DTSTAMP:20260116T211701
CREATED:20260101T042836Z
LAST-MODIFIED:20260101T042836Z
UID:8479-1767621600-1767625200@isrt.ac.bd
SUMMARY:Applied Statistics and Data Science Seminar on Monday 5 January 2026
DESCRIPTION:Title: A Moment-Based Generalization To Post-Prediction Inference \nVenue\, date and time: ISRT\, 5 January 2026\, 2 pm \nSpeaker: Awan Afiaz\, PhD candidate at the Department of Biostatistics\, Deltin 7 Aviator গেম টাকা ইনকাম of Washington Seattle\, WA\, USA and ISRT alumnus \nAbstract: \nAs artificial intelligence (AI) and machine learning (ML) become increasingly integrated into scientific research\, investigators frequently substitute predicted outcomes for expensive or difficult-to-measure data. However\, treating these AI/ML-generated predictions as true observations can lead to biased estimates and anti-conservative inference. While high predictive accuracy is often assumed to ensure valid downstream inference\, statistical challenges in inference with predicted data (IPD) fundamentally reduce to two sources of error: bias\, when predictions systematically distort relationships among variables\, and variance\, when uncertainty from prediction models is inadequately propagated. Wang et al. (2020) introduced post-prediction inference (PostPI)\, a pioneering method that addresses this challenge by modeling the relationship between predicted and observed outcomes in a small gold-standard dataset to calibrate inference in larger unlabeled samples. PostPI has been influential in formalizing the IPD problem and demonstrating how naive approaches fail to appropriately reflect uncertainty. However\, PostPI relies on a critical assumption: that prediction errors are uncorrelated with covariates of interest. In realistic settings where prediction algorithms exhibit systematic errors related to input features\, this assumption is often violated\, leading to biased parameter estimates and inadequate error control. We revisit PostPI in light of recent methodological advances and propose a moment-based generalization that relaxes this restrictive assumption. Our extension explicitly accounts for the covariance between prediction errors and covariates by incorporating an additional correction term estimated from the labeled dataset. This approach yields unbiased point estimates under standard conditions while incorporating a simple scaling factor that appropriately reflects the contribution of relationship model uncertainty regardless of sample size allocation. Through extensive simulations across three data-generating scenarios\, we demonstrate that our method maintains nominal Type-I error rates and achieves proper coverage probability\, even when the labeled sample is substantially smaller than the unlabeled sample settings where both naive approaches and original PostPI fail. Our work illustrates the classic bias-variance trade-off inherent to IPD’s challenges and confirms that there is no free lunch when substituting predicted outcomes for true measurements.
URL:https://isrt.ac.bd/event/applied-statistics-and-data-science-seminar-on-monday-5-january-2026/
CATEGORIES:seminar
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