5 papers
Strong Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in -Largest Generalized Extreme-Value Models
Jihong Park, Yire Shin, Jeong-Soo Park
We study maximum likelihood estimation in the three-parameter -largest generalized extreme-value (GEV) model with a fixed number of upper order statistics per block. Beca…
Fisher Information and Differentiability for -Largest Generalized Extreme-Value Models
Jihong Park, Yire Shin, Jeong-Soo Park
The largest observations of a block form a dependent random vector whose support moves with all three parameters of the -largest generalized extreme-value (GEV) model. Fo…
Automated selection of in -largest order statistics models: finite-sample performance and return-level inference
Yire Shin, Jihong Park, Jeong-Soo Park
Selecting the number of upper order statistics is a key practical issue in fitting -largest order statistics models. The entropy difference test for selecting relies on…
LocDreamer: World Model-Based Learning for Joint Indoor Tracking and Anchor Scheduling
Geng Wang, Zhouyou Gu, Shenghong Li +4
Accurate, resource-efficient localization and tracking enables numerous location-aware services in next-generation wireless networks. However, existing machine learning-based metho…
Generalized method of L-moment estimation for stationary and nonstationary extreme value models
Yonggwan Shin, Yire Shin, Jihong Park +1
Precisely estimating out-of-sample upper quantiles is very important in risk assessment and in engineering practice for structural design to prevent a greater disaster. For this pu…