33 citations · 47 across the 26 of their papers we have counts for
29 papers · 1 filter
Spatial function-on-function quantile regression
Eylul Fidan, Ufuk Beyaztas, Soutir Bandyopadhyay
This paper introduces a novel penalized spatial function-on-function quantile regression framework for analyzing spatially indexed functional data, bridging a critical gap between…
Robust spatial scalar-on-function regression: A Fisher-consistent redescending M-estimation approach
Muge Mutis, Ufuk Beyaztas, Han Lin Shang
We develop a Fisher-consistent redescending robust estimator for the spatial scalar-on-function regression model, where a scalar response depends on both a functional predictor and…
Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression
Caihong Qin, Lan Xue, Ufuk Beyaztas +4
Wearable devices collect time-varying biobehavioral data, offering opportunities to investigate how behaviors influence health outcomes. However, these data often contain measureme…
Penalized spatial function-on-function regression
Ufuk Beyaztas, Han Lin Shang, Gizel Bakicierler Sezer
The function-on-function regression model is fundamental for analyzing relationships between functional covariates and responses. However, most existing function-on-function regres…
Addressing zero-inflated and mis-measured functional predictors in scalar-on-function regression model
Heyang Ji, Lan Xue, Ufuk Beyaztas +4
Wearable devices are often used in clinical and epidemiological studies to monitor physical activity behavior and its influence on health outcomes. These devices are worn over mult…
MECfda: An R Package for Bias Correction Due to Measurement Error in Functional and Scalar Covariates in Scalar-on-Function Regression Models
Heyang Ji, Ufuk Beyaztas, Nicolas Escobar-Velasquez +6
Functional data analysis (FDA) deals with high-resolution data recorded over a continuum, such as time, space or frequency. Device-based assessments of physical activity or sleep a…
Spatial Scalar-on-Function Quantile Regression Model
Muge Mutis, Ufuk Beyaztas, Filiz Karaman +1
This paper introduces a novel spatial scalar-on-function quantile regression model that extends classical scalar-on-function models to account for spatial dependence and heterogene…
Robust Functional Logistic Regression
Berkay Akturk, Ufuk Beyaztas, Han Lin Shang
Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parame…
On function-on-function linear quantile regression
Muge Mutis, Ufuk Beyaztas, Filiz Karaman +1
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-…
Least squares-based methods to bias adjustment in scalar-on-function regression model using a functional instrumental variable
Xiwei Chen, Ufuk Beyaztas, Caihong Qin +5
Instrumental variables are widely used to adjust for measurement error bias when assessing associations of health outcomes with ME prone independent variables. IV approaches addres…
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…
Stock Return Prediction based on a Functional Capital Asset Pricing Model
Ufuk Beyaztas, Kaiying Ji, Han Lin Shang +1
The capital asset pricing model (CAPM) is readily used to capture a linear relationship between the daily returns of an asset and a market index. We extend this model to an intrada…
Penalized function-on-function linear quantile regression
Ufuk Beyaztas, Han Lin Shang, Semanur Saricam
We introduce a novel function-on-function linear quantile regression model to characterize the entire conditional distribution of a functional response for a given functional predi…
Functional Linear Cox Regression Model with Frailty
Deniz Inan, Ufuk Beyaztas, Carmen D. Tekwe +2
This paper presents a functional linear Cox regression model with frailty to tackle unobserved heterogeneity in survival data with functional covariates. While traditional Cox mode…
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…
Enhancing Spatial Functional Linear Regression with Robust Dimension Reduction Methods
Ufuk Beyaztas, Abhijit Mandal, Han Lin Shang
This paper introduces a robust estimation strategy for the spatial functional linear regression model using dimension reduction methods, specifically functional principal component…
Robust function-on-function interaction regression
Ufuk Beyaztas, Han Lin Shang, Abhijit Mandal
A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model's pa…
A robust scalar-on-function logistic regression for classification
Muge Mutis, Ufuk Beyaztas, Gulhayat Golbasi Simsek +1
Scalar-on-function logistic regression, where the response is a binary outcome and the predictor consists of random curves, has become a general framework to explore a linear relat…
A Robust Functional Partial Least Squares for Scalar-on-Multiple-Function Regression
Ufuk Beyaztas, Han Lin Shang
The scalar-on-function regression model has become a popular analysis tool to explore the relationship between a scalar response and multiple functional predictors. Most of the exi…
Function-on-function linear quantile regression
Ufuk Beyaztas, Han Lin Shang
In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approac…
A robust partial least squares approach for function-on-function regression
Ufuk Beyaztas, Han Lin Shang
The function-on-function linear regression model in which the response and predictors consist of random curves has become a general framework to investigate the relationship betwee…
Function-on-function partial quantile regression
Ufuk Beyaztas, Han Lin Shang, Aylin Alin
In this paper, a functional partial quantile regression approach, a quantile regression analog of the functional partial least squares regression, is proposed to estimate the funct…
A partial least squares approach for function-on-function interaction regression
Ufuk Beyaztas, Han Lin Shang
A partial least squares regression is proposed for estimating the function-on-function regression model where a functional response and multiple functional predictors consist of ra…
Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models
Ufuk Beyaztas, Han Lin Shang
The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying…