19 citations · 48 across the 8 of their papers we have counts for
8 papers
Weighted Generalized Risk Measure and Risk Quadrangle: Characterization, Optimization and Application
Yang Liu, Yunran Wei, Xintao Ye
Various financial market scenarios may cause heterogeneous risk assessments among analysts, which motivates the usage of the Generalized Risk Measure in Fadina et al. (2024, Financ…
A General Framework for Cutting Feedback within Modularised Bayesian Inference
Yang Liu, Robert J. B. Goudie
Standard Bayesian inference can build models that combine information from various sources, but this inference may not be reliable if components of a model are misspecified. Cut in…
A Geometric Perspective on Bayesian and Generalized Fiducial Inference
Yang Liu, Jan Hannig, Alexander C Murph
Post-data statistical inference concerns making probability statements about model parameters conditional on observed data. When a priori knowledge about parameters is available, p…
Anonymized risk sharing
Zhanyi Jiao, Steven Kou, Yang Liu +1
Anonymized risk sharing requires no information about agents' preferences, identities, private operations, or realized losses. It is especially relevant in the digital economy, wit…
A Framework for Measures of Risk under Uncertainty
Tolulope Fadina, Yang Liu, Ruodu Wang
A risk analyst assesses potential financial losses based on multiple sources of information. Often, the assessment does not only depend on the specification of the loss random vari…
Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework
Yang Liu, Robert J. B. Goudie
Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its g…
Ordering and Inequalities for Mixtures on Risk Aggregation
Yuyu Chen, Peng Liu, Yang Liu +1
Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation. In this paper, we investigate orderi…
Stochastic Approximation Cut Algorithm for Inference in Modularized Bayesian Models
Yang Liu, Robert J. B. Goudie
Bayesian modelling enables us to accommodate complex forms of data and make a comprehensive inference, but the effect of partial misspecification of the model is a concern. One app…