arXiv subjects

Liming Xiang

Publications and source records attributed to Liming Xiang.

3 recordsLinked to original sources

arXiv2026

Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis

Cardiovascular diseases are major causes of mortality globally. They often co-occur and are interrelated, leading to partial-order relationships among their onset times. However, these onset times are subject to informative censoring due to the occurrence of death, posing significant challenges for survival prediction. In this article, we propose a novel copula-based framework that learns dependence among multiple correlated marginal components through a pseudo-likelihood for estimation. We adopt nonparametric marginals, alleviating the reliance on marginal distribution assumptions typically required in conventional copula models, and estimate the association between the onsets of intermediate cardiovascular diseases and death by solving a concordance estimating equation. Under this framework, a renewable risk assessment method is developed for dynamic survival prediction, leveraging information on disease onset times and the maximum follow-up duration. Our proposed method yields estimators with well-established properties, and its flexibility and predictive effectiveness are demonstrated through extensive simulation studies. We apply the method to data from a heart disease study, demonstrating the benefits of incorporating the associations among various cardiovascular diseases and their synergistic effects on mortality for dynamic prediction of overall survival.

stat.ME↗

arXiv2025

{\delta}-EMG: A Monotonic Graph Index for Approximate Nearest Neighbor Search

Approximate nearest neighbor (ANN) search in high-dimensional spaces is a foundational component of many modern retrieval and recommendation systems. Currently, almost all algorithms follow an $\epsilon$-Recall-Bounded principle when comparing performance: they require the ANN search results to achieve a recall of more than $1-\epsilon$ and then compare query-per-second (QPS) performance. However, this approach only accounts for the recall of true positive results and does not provide guarantees on the deviation of incorrect results. To address this limitation, we focus on an Error-Bounded ANN method, which ensures that the returned results are a $(1/\delta)$-approximation of the true values. Our approach adopts a graph-based framework. To enable Error-Bounded ANN search, we propose a $\delta$-EMG (Error-bounded Monotonic Graph), which, for the first time, provides a provable approximation for arbitrary queries. By enforcing a $\delta$-monotonic geometric constraint during graph construction, $\delta$-EMG ensures that any greedy search converges to a $(1/\delta)$-approximate neighbor without backtracking. Building on this foundation, we design an error-bounded top-$k$ ANN search algorithm that adaptively controls approximation accuracy during query time. To make the framework practical at scale, we introduce $\delta$-EMQG (Error-bounded Monotonic Quantized Graph), a localized and degree-balanced variant with near-linear construction complexity. We further integrate vector quantization to accelerate distance computation while preserving theoretical guarantees. Extensive experiments on the ANN-Benchmarks dataset demonstrate the effectiveness of our approach. Under a recall requirement of 0.99, our algorithm achieves 19,000 QPS on the SIFT1M dataset, outperforming other methods by more than 40\%.

cs.IR↗

arXiv2025

Quantile Residual Lifetime Regression for Multivariate Failure Time Data

The quantile residual lifetime (QRL) regression is an attractive tool for assessing covariate effects on the distribution of residual life expectancy, which is often of interest in clinical studies. When the study subjects are exposed to multiple events of interest, the failure times observed for the same subject are potentially correlated. To address such correlation in assessing the covariate effects on QRL, we propose a marginal semiparametric QRL regression model for multivariate failure time data. Our new proposal facilitates estimation of the model parameters using unbiased estimating equations and results in estimators, which are shown to be consistent and asymptotically normal. To overcome additional challenges in inference, we provide three methods for variance estimation based on resampling techniques and a sandwich estimator, and further develop a Wald-type test statistic for inference. The simulation studies and a real data analysis offer evidence of the satisfactory performance of the proposed method.

stat.ME↗