Shrinkage Estimation

Latest papers

11 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management
Oct 20, 2026Recep Yusuf Bekci, Stefanus Jasin, Joline UichancoShrinkage Estimation Transfer Learning
Department of Management Science and Engineering, University of Waterloo, Waterloo, Ontario, Canada · Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan, USA · Department of Technology Management and Innovation, Tandon School of Engineering, New York University, Brooklyn, New York, USA
Incentive Alignment in Online Experimentation
Oct 5, 2026Ermis Soumalias, Richard Mudd, Abbas ZaidiCausal Effect Estimation A/b Testing
Meta Platforms, Inc.
Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation
Sep 5, 2026Shutong Zheng, Sijia ChenShrinkage Estimation Federated Graph Learning
Sun Yat-sen University · The Hong Kong University of Science and Technology (Guangzhou)
A Simple Approximation to the Distribution of the Ridge Regression Estimator
Aug 3, 2026José Luis Montiel Olea, Ryan Strong, Amilcar Velez +2Ridge Regression Hyperparameter Optimization
Department of Economics at Cornell University
XMSE-Aware Adaptive Empirical Bayes Estimation
Jun 25, 2026Minghao Chen, Jiale ZhengParameter Estimation Empirical Bayes
Tencent Technology, Shenzhen, China · Huawei Noah’s Ark Lab, Shenzhen, China
SPADE: Structure-Prior Adaptive Decision Estimation
Jun 22, 2026Yifan WangModel Selection Scientific ML
Department of Mechanical Engineering, McGill University · QC, H3A 2T7, Canada
Covariance Shrinkage via Stochastic Interpolation
Jun 5, 2026Mathieu Chalvidal, Florentin Coeurdoux, Eric Vanden-EijndenStochastic Interpolants Covariance Estimation
Capital Fund Management 23 Rue de l’Universit´e, 75007 Paris
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
May 18, 2026Radu Lecoiu, Debarghya Mukherjee, Pragya SurSelf-Distillation Spectral Methods
Department of Statistics, Harvard University · Department of Mathematics & Statistics, Boston University
From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators
Mar 20, 2026Karun Adusumilli, Maximilian Kasy, Ashia WilsonCross-Validation Shrinkage Estimation
Department of Economics, University of Pennsylvania. · Department of Economics, University of Oxford. · Department of Electrical Engineering and Computer Science, MIT.
Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Feb 23, 2026Joel Persson, Jurriën Bakker, Dennis Bohle +2Shrinkage Estimation Causal Effect Estimation
Spotify · Munich Center for Machine Learning & LMU Munich · ETH Zurich
Estimation of multiple mean vectors in high dimension
Mar 22, 2024Gilles Blanchard, Jean-Baptiste Fermanian, Hannah MarienwaldKernel Mean Embeddings Statistical Learning Theory
LMO, DATASHAPE · Université Paris Saclay, Institut Mathématique d’Orsay, France. · LMO +2

Oct 20, 2026J/K move · Enter open · S save

Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management

Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco

Department of Management Science and Engineering, University of Waterloo, Waterloo, Ontario, Canada · Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan, USA · Department of Technology Management and Innovation, Tandon School of Engineering, New York University, Brooklyn, New York, USA

Shrinkage Estimation Transfer Learning Discrete Choice Modeling

Open paper AskPDF arXiv

Large retail chains must tailor assortment and pricing decisions to local customer preferences, yet even retailers with extensive sales records often have limited data for estimating demand at the store or local market level, particularly in product categories with short selling windows. Local estimation (fitting a demand model for each store on its own) can therefore be unreliable, while pooling data across stores improves estimation precision at the cost of ignoring local demand heterogeneity. We study how retailers can balance these competing effects through transfer learning under a multinomial logit (MNL) demand model. We propose a shrinkage estimator that combines store-specific demand estimates with information pooled from other stores. Unlike the linear or quadratic settings underlying much of the existing theoretical literature on information transfer in operations, the nonlinear MNL likelihood does not admit a straightforward bias-variance decomposition. By exploiting its self-concordant-like properties, we establish finite-sample performance guarantees that characterize when information transfer improves demand estimation and how the appropriate degree of shrinkage depends on local sample size, feature dimensionality, and cross-store heterogeneity. We further derive regret bounds for downstream assortment and pricing decisions. Numerical experiments demonstrate the operational benefits of shrinkage across different data environments. An empirical study using data from several hundred stores of a European apparel retailer shows that shrinkage improves out-of-sample product prioritization and revenue capture relative to both local estimation and full pooling. Our findings demonstrate how retailers can leverage information across their store networks to improve revenue management decisions while preserving local demand differences.