4 citations · 7 across the 5 of their papers we have counts for
7 papers
Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
Joel Persson, Mårten Schultzberg, Sebastian Ankargren
Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at…
Risk-aware product decisions in A/B tests with multiple metrics
Mårten Schultzberg, Sebastian Ankargren, Mattias Frånberg
In the past decade, AB tests have become the standard method for making product decisions in tech companies. They offer a scientific approach to product development, using statisti…
Resampling-free bootstrap inference for quantiles
Mårten Schultzberg, Sebastian Ankargren
Bootstrap inference is a powerful tool for obtaining robust inference for quantiles and difference-in-quantiles estimators. The computationally intensive nature of bootstrap infere…
Local Prediction Pools
Oscar Oelrich, Mattias Villani, Sebastian Ankargren
We propose local prediction pools as a method for combining the predictive distributions of a set of experts conditional on a set of variables believed to be related to the predict…
Estimating Large Mixed-Frequency Bayesian VAR Models
Sebastian Ankargren, Paulina Jonéus
We discuss the issue of estimating large-scale vector autoregressive (VAR) models with stochastic volatility in real-time situations where data are sampled at different frequencies…
A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior
Sebastian Ankargren, Måns Unosson, Yukai Yang
We propose a Bayesian vector autoregressive (VAR) model for mixed-frequency data. Our model is based on the mean-adjusted parametrization of the VAR and allows for an explicit prio…