This package presents robust methods for analyzing functional linear regression.
Ufuk Beyaztas and Han Lin Shang
Maintainer: Ufuk Beyaztas <ufukbeyaztas@gmail.com>
U. Beyaztas, A. Mandal and H. L. Shang (2026+) Enhancing spatial functional linear regression with robust dimension reduction methods, Journal of Multivariate Analysis, in press.
U. Beyaztas, H. L. Shang and S. Saricam (2025) Penalized function-on-function linear quantile regression, Computational Statistics, 40, 301-329.
B. Akturk, U. Beyaztas, H. L. Shang, A. Mandal (2025) Robust functional logistic regression, Advances in Data Analysis and Classification, 19, 121-145.
U. Beyaztas, H. L. Shang and A. Mandal (2025) Robust function-on-function interaction regression, Statistical Modelling: An International Journal, 25(3), 195-215.
M. Mutis, U. Beyaztas, F. Karaman and H. L. Shang (2025) On function-on-function linear quantile regression, Journal of Applied Statistics, 52(4), 814-840.
M. Mutis, U. Beyaztas, G. G. Simsek, H. L. Shang and Z. M. Yaseen (2024) Development of functional quantile autoregressive model for river flow curve forecasting, Earth and Space Science, 11, e2024EA003564.
S. Gurer, H. L. Shang, A. Mandal and U. Beyaztas (2024) Locally sparse and robust partial least squares in scalar-on-function regression, Statistics and Computing, 34, article number 150.
U. Beyaztas, M. Tez and H. L. Shang (2024) Robust scalar-on-function partial quantile regression, Journal of Applied Statistics, 51(7), 1359-1377.
U. Beyaztas and H. L. Shang (2023) Robust functional linear regression models, The R Journal, 15(1), 212-233.
M. Mutis, U. Beyaztas, G. G. Simsek and H. L. Shang (2023) A robust scalar-on-function logistic regression for classification, Communications in Statistics - Theory and Methods, 52(23), 8538-8554.
U. Beyaztas and H. L. Shang (2022) Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models, Journal of Applied Statistics, 49(5), 1179-1202.
U. Beyaztas, H. L. Shang and A. G. Abdel-Salam (2022) Functional linear model for interval-valued data, Communications in Statistics - Simulation and Computation, 51(7), 3513-3532.
U. Beyaztas and H. L. Shang (2022) Machine-learning-based functional time series forecasting: Application to age-specific mortality rates, Forecasting, 4, 394-408.
S. Saricam, U. Beyaztas, B. Asikgil and H. L. Shang (2022) On partial least-squares estimation in scalar-on-function regression models, Journal of Chemometrics, 36(12), e3452.
U. Beyaztas and H. L. Shang (2022) A comparison of parameter estimation in function-on-function regression, Communications in Statistics - Simulation and Computation, 51(8), 4607-4637.
U. Beyaztas and H. L. Shang (2022) A robust functional partial least squares for scalar-on-multiple-function regression, Journal of Chemometrics, 36(4), e3394.
U. Beyaztas, H. L. Shang and A. Alin (2022) Function-on-function partial quantile regression, Journal of Agricultural, Biological, and Environmental Statistics, 27(1), 149-174.
U. Beyaztas, H. L. Shang and Z. M. Yaseen (2021) A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction, Journal of Hydrology, 598, 126380.
U. Beyaztas and H. L. Shang (2021) A partial least squares approach for function-on-function interaction regression, Computational Statistics, 36(2), 911-939.
U. Beyaztas and H. L. Shang (2021) A robust partial least squares approach for function-on-function regression, Brazilian Journal of Probability and Statistics, 36(2), 199-219.
U. Beyaztas and H. L. Shang (2021) Function-on-function linear quantile regression, Mathematical Modelling and Analysis, 27(2), 322-341.
U. Beyaztas and H. L. Shang (2020) On function-on-function regression: partial least squares approach, Environmental and Ecological Statistics, 27(1), 95-114.
U. Beyaztas and H. L. Shang (2019) Forecasting functional time series using weighted likelihood methodology, Journal of Statistical Computation and Simulation, 89(16), 3046-3060.