output
20142025
most citedThe short-term effect of COVID-19 pandemic on China's crude oil futures market: A study based on multifractal analysis

20 citations

18 papers

stat.ME2025★ 7 cited

Hypothesis testing for quantitative trait locus effects in both location and scale in genetic backcross studies

Guanfu Liu, Pengfei Li, Yukun Liu +1

Testing the existence of a quantitative trait locus (QTL) effect is an important task in QTL mapping studies. Most studies concentrate on the case where the phenotype distributions…

math.ST2025★ 7 cited

On consistency of the MLE under finite mixtures of location-scale distributions with a structural parameter

Guanfu Liu, Pengfei Li, Yukun Liu +1

We provide a general and rigorous proof for the strong consistency of maximum likelihood estimators of the cumulative distribution function of the mixing distribution and structura…

stat.ME2025★ 2 cited

Two-step semiparametric empirical likelihood inference from capture-recapture data with missing covariates

Yang Liu, Yukun Liu, Pengfei Li +1

Missing covariates are not uncommon in capture-recapture studies. When covariate information is missing at random in capture-recapture data, an empirical full likelihood method has…

stat.ME2024

SID: A Novel Class of Nonparametric Tests of Independence for Censored Outcomes

Jinhong Li, Jicai Liu, Jinhong You +1

We propose a new class of metrics, called the survival independence divergence (SID), to test dependence between a right-censored outcome and covariates. A key technique for derivi…

math.DS2024

Dynamic asymptotic dimension growth for group actions and groupoids

Hang Wang, Yanru Wang, Jianguo Zhang +1

We introduce the notion of dynamic asymptotic dimension growth for actions of discrete groups on compact spaces, and more generally for locally compact étale groupoids. Moreover, w…

stat.CO2024

Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction

Yuan Gao, Rui Pan, Feng Li +2

Kernel smoothing is a widely used nonparametric method in modern statistical analysis. The problem of efficiently conducting kernel smoothing for a massive dataset on a distributed…