17 citations · 37 across the 9 of their papers we have counts for
10 papers
Anytime-Valid Inference in Conjoint Experiments
Daniel Molitor, Jennah Gosciak, Michael Lindon
Choosing an appropriate sample size is a fundamental challenge in experimental design. This challenge is particularly salient in conjoint experiments, where researchers simultaneou…
Design-Based Anytime-Valid Inference for Randomized Experiments with Delayed Outcomes and Staggered Entry
Michael Lindon, Nathan Kallus
Delayed outcomes are ubiquitous in online experimentation: treatment can affect whether an outcome occurs, when it occurs, and its realized value. To accommodate staggered entry wh…
Anytime-Valid Continuous-Time Confidence Processes for Inhomogeneous Poisson Processes
Michael Lindon, Nathan Kallus
Motivated by monitoring the arrival of incoming adverse events such as customer support calls or crash reports from users exposed to an experimental product change, we consider seq…
Design-Based Inference for Multi-arm Bandits
Dae Woong Ham, Iavor Bojinov, Michael Lindon +1
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online d…
Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
Aurelien Bibaut, Nathan Kallus, Michael Lindon
Sequential tests and their implied confidence sequences, which are valid at arbitrary stopping times, promise flexible statistical inference and on-the-fly decision making. However…
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Panel Experiments
Dae Woong Ham, Iavor Bojinov, Michael Lindon +1
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. Beyond aiding managerial decision-making, experiments…
Anytime-Valid Linear Models and Regression Adjusted Causal Inference in Randomized Experiments
Michael Lindon, Dae Woong Ham, Martin Tingley +1
Linear models are foundational tools in statistics and ubiquitous across the applied sciences. However, conventional statistical inference -- such as -tests and -tests -- are…
Rapid Regression Detection in Software Deployments through Sequential Testing
Michael Lindon, Chris Sanden, Vaché Shirikian
The practice of continuous deployment has enabled companies to reduce time-to-market by increasing the rate at which software can be deployed. However, deploying more frequently be…
Anytime-Valid Inference for Multinomial Count Data
Michael Lindon, Alan Malek
Many experiments are concerned with the comparison of counts between treatment groups. Examples include the number of successful signups in conversion rate experiments, or the numb…
Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic Models
Isaac Lavine, Michael Lindon, Mike West
We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of multivariate time series. The perspective is that of a decision-maker with a specif…