arXiv subjects

Ruixuan Rachel Zhou

Publications and source records attributed to Ruixuan Rachel Zhou.

3 recordsLinked to original sources

arXiv2026

Confidence Intervals for Rate Estimation with Importance Sampling in Autonomous Vehicle Evaluation

Accounting for both rare events and complex sampling presents challenges when quantifying uncertainty for rate estimation in autonomous vehicle performance evaluation. In this paper, we introduce a statistical formulation of this problem and develop a unified compound Poisson model framework for unbiased rate estimation through the Horvitz Thompson estimator. Though asymptotic theory for the model is available, the inference of confidence intervals (CIs) in the presence of rare events requires new investigation. We also advocate for a new monotonicity criterion for rate CIs--summing the rates of disjoint types of events should produce not only a higher point estimate but also higher confidence bounds than for the individual rates--that facilitates interpretability in real applications. We propose a novel exponential bootstrap (EB) method for CI construction based on a fiducial argument; it satisfies the monotonicity property, while novel extensions of some existing methods do not. Comprehensive numerical studies show that EB performs well for a wide range of settings relevant to our applications. Fast implementation of EB based on saddlepoint approximation is also developed, which may be of independent interest.

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arXiv2023

Behavioral event detection and rate estimation for autonomous vehicle evaluation

Autonomous vehicles are continually increasing their presence on public roads. However, before any new autonomous driving software can be approved, it must first undergo a rigorous assessment of driving quality. These quality evaluations typically focus on estimating the frequency of (undesirable) behavioral events. While rate estimation would be straight-forward with complete data, in the autonomous driving setting this estimation is greatly complicated by the fact that \textit{detecting} these events within large driving logs is a non-trivial task that often involves human reviewers. In this paper we outline a \textit{streaming partial tiered event review} configuration that ensures both high recall and high precision on the events of interest. In addition, the framework allows for valid streaming estimates at any phase of the data collection process, even when labels are incomplete, for which we develop the maximum likelihood estimate and show it is unbiased. Constructing honest and effective confidence intervals (CI) for these rate estimates, particularly for rare safety-critical events, is a novel and challenging statistical problem due to the complexity of the data likelihood. We develop and compare several CI approximations, including a novel Gamma CI method that approximates the exact but intractable distribution with a weighted sum of independent Poisson random variables. There is a clear trade-off between statistical coverage and interval width across the different CI methods, and the extent of this trade-off varies depending on the specific application settings (e.g., rare vs. common events). In particular, we argue that our proposed CI method is the best-suited when estimating the rate of safety-critical events where guaranteed coverage of the true parameter value is a prerequisite to safely launching a new ADS on public roads.

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arXiv2019

Estimation and inference for the indirect effect in high-dimensional linear mediation models

Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, as well as complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.

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