#  Wenqi Shi (Applied Stats Workshop) 

 



####  calendar\_today Date and Time 

 **February 25, 2026** 

 12:00PM - 01:30PM EST 

####  pin\_drop Location 

 **CGIS Knafel Building, Room K354**  



 

 [ Join via Zoom arrow\_circle\_right ](https://harvard.zoom.us/j/93110218231?pwd=Gmka2cTdUty8AcWec90hWmcSllXtkP.1) 

 



 

### Speaker & Title

Wenqi Shi, "Meta-analysis through Low-Rank Basis Hunting"

### Abstract

A central challenge of meta-analysis is that the populations underlying existing studies often differ from the target population in unknown ways. We study the problem of predicting function-valued quantities, such as regression and conditional average treatment effect functions, for a new target population using only study-level covariates and estimates. Our approach assumes a shared low-rank structure, in which the true function from each study lies within the convex hull of a small set of latent basis functions. To recover these basis functions, we extend the Successive Projection Algorithm to the functional setting, incorporating a denoised basis-hunting step. We then model the relationship between study-level covariates and the corresponding mixing weights using flexible semi-parametric or non-parametric methods. The proposed framework is privacy-preserving and enables meta-analytic prediction based on study-level information alone even when individual-level data are unavailable to analysts. In addition, for each study, functions of interest can be estimated using possibly different machine learning algorithms. For uncertainty quantification, we construct prediction intervals via conformal prediction. We show that, under exchangeability and mild estimation-error conditions, these intervals achieve asymptotically valid marginal coverage. We demonstrate the effectiveness of the proposed methodology through both simulation studies and empirical applications.



 

The Applied Statistics Workshop (Gov 3009) meets all academic year on Wednesdays. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.  
  
All interested Harvard affiliates are invited to attend. Lunch will be provided.



 

 



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