b'Han Lin Shang'

research

∙ 05/25/2023

Forecasting intraday financial time series with sieve bootstrapping and dynamic updating

Intraday financial data often take the form of a collection of curves th...

0 Han Lin Shang, et al. ∙

research

∙ 04/14/2023

Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series

In this paper, we consider detecting and estimating breaks in heterogene...

0 Degui Li, et al. ∙

research

∙ 04/04/2023

A nonlinearity and model specification test for functional time series

An important issue in functional time series analysis is whether an obse...

0 Xin Huang, et al. ∙

research

∙ 12/08/2022

Fractionally integrated curve time series with cointegration

We introduce methods and theory for fractionally cointegrated curve time...

0 Won-Ki Seo, et al. ∙

research

∙ 04/05/2022

A robust scalar-on-function logistic regression for classification

Scalar-on-function logistic regression, where the response is a binary o...

0 Muge Mutis, et al. ∙

research

∙ 03/09/2022

A Robust Functional Partial Least Squares for Scalar-on-Multiple-Function Regression

The scalar-on-function regression model has become a popular analysis to...

0 Ufuk Beyaztas, et al. ∙

research

∙ 03/09/2022

Factor-augmented model for functional data

We propose modeling raw functional data as a mixture of a smooth functio...

0 Yuan Gao, et al. ∙

research

∙ 01/04/2022

Clustering and Forecasting Multiple Functional Time Series

Modelling and forecasting homogeneous age-specific mortality rates of mu...

0 Chen Tang, et al. ∙

research

∙ 12/16/2021

A model sufficiency test using permutation entropy

Using the ordinal pattern concept in permutation entropy, we propose a m...

0 Xin Huang, et al. ∙

research

∙ 12/01/2021

AR-sieve Bootstrap for High-dimensional Time Series

This paper proposes a new AR-sieve bootstrap approach on high-dimensiona...

0 Daning Bi, et al. ∙

research

∙ 11/09/2021

Function-on-function linear quantile regression

In this study, we propose a function-on-function linear quantile regress...

0 Ufuk Beyaztas, et al. ∙

research

∙ 11/08/2021

Is the group structure important in grouped functional time series?

We study the importance of group structure in grouped functional time se...

0 Yang Yang, et al. ∙

research

∙ 11/01/2021

A robust partial least squares approach for function-on-function regression

The function-on-function linear regression model in which the response a...

0 Ufuk Beyaztas, et al. ∙

research

∙ 09/13/2021

Function-on-function partial quantile regression

In this paper, a functional partial quantile regression approach, a quan...

0 Ufuk Beyaztas, et al. ∙

research

∙ 09/09/2021

Multi-population mortality forecasting using high-dimensional functional factor models

This paper proposes a two-fold factor model for high-dimensional functio...

0 Chen Tang, et al. ∙

research

∙ 07/22/2021

Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces

This paper presents static and dynamic versions of univariate, multivari...

0 Han Lin Shang, et al. ∙

research

∙ 06/02/2021

Feature Extraction for Functional Time Series: Theory and Application to NIR Spectroscopy Data

We propose a novel method to extract global and local features of functi...

0 Yang Yang, et al. ∙

research

∙ 04/26/2021

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

In this research, a functional time series model was introduced to predi...

0 Ufuk Beyaztas, et al. ∙

research

∙ 02/04/2021

Factor-augmented Smoothing Model for Functional Data

We propose modeling raw functional data as a mixture of a smooth functio...

0 Yuan Gao, et al. ∙

research

∙ 02/01/2021

Double bootstrapping for visualising the distribution of descriptive statistics of functional data

We propose a double bootstrap procedure for reducing coverage error in t...

0 Han Lin Shang, et al. ∙

research

∙ 12/19/2020

Functional time series forecasting of extreme values

We consider forecasting functional time series of extreme values within ...

0 Han Lin Shang, et al. ∙

research

∙ 12/09/2020

A partial least squares approach for function-on-function interaction regression

A partial least squares regression is proposed for estimating the functi...

0 Ufuk Beyaztas, et al. ∙

research

∙ 11/16/2020

Change point detection for COVID-19 excess deaths in Belgium

Emerging at the end of 2019, COVID-19 has become a public health threat ...

0 Han Lin Shang, et al. ∙

research

∙ 11/16/2020

Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models

The bootstrap procedure has emerged as a general framework to construct ...

0 Ufuk Beyaztas, et al. ∙

research

∙ 11/09/2020

Bayesian bandwidth estimation for local linear fitting in nonparametric regression models

This paper presents a Bayesian sampling approach to bandwidth estimation...

0 Han Lin Shang, et al. ∙

research

∙ 10/11/2020

Granger causality of bivariate stationary curve time series

We study causality between bivariate curve time series using the Granger...

0 Han Lin Shang, et al. ∙

research

∙ 08/19/2020

Synergy in fertility forecasting: Improving forecast accuracy through model averaging

Accuracy in fertility forecasting has proved challenging and warrants re...

0 Han Lin Shang, et al. ∙

research

∙ 07/07/2020

Forecasting Australian subnational age-specific mortality rates

When modeling sub-national mortality rates, it is important to incorpora...

0 Han Lin Shang, et al. ∙

research

∙ 06/25/2020

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

An essential input of annuity pricing is the future retiree mortality. F...

0 Han Lin Shang, et al. ∙

research

∙ 04/08/2020

Bootstrap Prediction Bands for Functional Time Series

A bootstrap procedure for constructing pointwise or simultaneous predict...

0 Efstathios Paparoditis, et al. ∙

research

∙ 03/17/2020

A comparison of Hurst exponent estimators in long-range dependent curve time series

The Hurst exponent is the simplest numerical summary of self-similar lon...

0 Han Lin Shang, et al. ∙

research

∙ 03/12/2020

A comparison of parameter estimation in function-on-function regression

Recent technological developments have enabled us to collect complex and...

0 Ufuk Beyaztas, et al. ∙

research

∙ 02/24/2020

Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density

This study examines the optimal selections of bandwidth and semi-metric ...

0 Han Lin Shang, et al. ∙

research

∙ 01/10/2020

Forecasting multiple functional time series in a group structure: an application to mortality

When modeling sub-national mortality rates, we should consider three fea...

0 Han Lin Shang, et al. ∙

research

∙ 01/08/2020

Functional linear models for interval-valued data

Aggregation of large databases in a specific format is a frequently used...

0 Ufuk Beyaztas, et al. ∙

research

∙ 12/15/2019

On function-on-function regression: Partial least squares approach

Functional data analysis tools, such as function-on-function regression ...

0 Ufuk Beyaztas, et al. ∙

research

∙ 09/21/2019

Implied volatility surface predictability: the case of commodity markets

Recent literature seek to forecast implied volatility derived from equit...

0 Fearghal Kearney, et al. ∙

research

∙ 09/01/2019

Dynamic principal component regression for forecasting functional time series in a group structure

When generating social policies and pricing annuity at national and subn...

0 Han Lin Shang, et al. ∙

research

∙ 08/05/2019

Forecasting age distribution of death counts: An application to annuity pricing

We consider a compositional data analysis approach to forecasting the ag...

0 Han Lin Shang, et al. ∙

research

∙ 08/01/2019

Forecasting functional time series using weighted likelihood methodology

Functional time series whose sample elements are recorded sequentially o...

0 Ufuk Beyaztas, et al. ∙

research

∙ 05/10/2019

Dynamic principal component regression: Application to age-specific mortality forecasting

In areas of application, including actuarial science and demography, it ...

0 Han Lin Shang, et al. ∙

research

∙ 01/17/2019

A robust functional time series forecasting method

Univariate time series often take the form of a collection of curves obs...

0 Han Lin Shang, et al. ∙

research

∙ 01/08/2019

Uncovering predictability in the evolution of the WTI oil futures curve

Accurately forecasting the price of oil, the world's most actively trade...

0 Fearghal Kearney, et al. ∙

research

∙ 11/30/2018

Intraday forecasts of a volatility index: Functional time series methods with dynamic updating

As a forward-looking measure of future equity market volatility, the VIX...

0 Han Lin Shang, et al. ∙

research

∙ 10/19/2018

Estimation of a functional single index model with dependent errors and unknown error density

The problem of error density estimation for a functional single index mo...

0 Han Lin Shang, et al. ∙

research

∙ 10/03/2018

Semiparametric Regression using Variational Approximations

Semiparametric regression offers a flexible framework for modeling non-l...

0 Francis K. C. Hui, et al. ∙

research

∙ 10/02/2018

High-dimensional functional time series forecasting: An application to age-specific mortality rates

We address the problem of forecasting high-dimensional functional time s...

0 Yuan Gao, et al. ∙

research

∙ 09/28/2018

Model confidence sets and forecast combination: An application to age-specific mortality

Model averaging combines forecasts obtained from a range of models, and ...

0 Han Lin Shang, et al. ∙

Han Lin Shang

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