activity
20192025
most citedRetiree mortality forecasting: A partial age-range or a full age-range model?

5 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ME2025

Constructing prediction intervals for the age distribution of deaths

Han Lin Shang, Steven Haberman

We introduce a model-agnostic procedure to construct prediction intervals for the age distribution of deaths. The age distribution of deaths is an example of constrained data, whic…

stat.ME2025

Spatial Functional Deep Neural Network Model: A New Prediction Algorithm

Merve Basaran, Ufuk Beyaztas, Han Lin Shang +1

Accurate prediction of spatially dependent functional data is critical for various engineering and scientific applications. In this study, a spatial functional deep neural network…

stat.ME2024

Spatial function-on-function regression

Ufuk Beyaztas, Han Lin Shang, Gizel Bakicierler Sezer +3

We introduce a spatial function-on-function regression model to capture spatial dependencies in functional data by integrating spatial autoregressive techniques with functional pri…

stat.ME20241 cited

Nonstationary functional time series forecasting

Han Lin Shang, Yang Yang

We propose a nonstationary functional time series forecasting method with an application to age-specific mortality rates observed over the years. The method begins by taking the fi…

stat.AP20205 cited

Retiree mortality forecasting: A partial age-range or a full age-range model?

Han Lin Shang, Steven Haberman

An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can be taken place in two routes. On the…

stat.AP2019

Uncovering predictability in the evolution of the WTI oil futures curve

Fearghal Kearney, Han Lin Shang

Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functi…