Ufuk Beyaztas
Ufuk Beyaztas
Department of Statistics, Marmara University
Verified email at marmara.edu.tr
Title
Cited by
Cited by
Year
Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
ZM Yaseen, AM Al-Juboori, U Beyaztas, N Al-Ansari, KW Chau, C Qi, ...
Engineering applications of computational fluid mechanics 14 (1), 70-89, 2020
162020
Construction of Prediction Intervals for Palmer Drought Severity Index Using Bootstrap
U Beyaztas, BB Arikan, BH Beyaztas, E Kahya
Journal of Hydrology 559, 461-470, 2018
142018
Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models
U Beyaztas, A Alin
Statistical Papers 55 (4), 1001-1018, 2014
142014
Drought interval simulation using functional data analysis
U Beyaztas, ZM Yaseen
Journal of Hydrology 579, 124141, 2019
132019
Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm
U Beyaztas, SQ Salih, KW Chau, N Al-Ansari, ZM Yaseen
Engineering Applications of Computational Fluid Mechanics 13 (1), 1165-1181, 2019
122019
Jackknife-after-bootstrap method for detection of influential observations in linear regression models
U Beyaztas, A Alin
Communications in Statistics-Simulation and Computation 42 (6), 1256-1267, 2013
92013
Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model
H Tao, AA Ewees, AO Al-Sulttani, U Beyaztas, MM Hameed, SQ Salih, ...
Energy Reports 7, 136-157, 2021
72021
On function-on-function regression: Partial least squares approach
U Beyaztas, HL Shang
Environmental and Ecological Statistics, 2020
72020
Sufficient m-out-of-n (m/n) bootstrap
A Alin, MA Martin, U Beyaztas, PK Pathak
Journal of Statistical Computation and Simulation 87 (9), 1742-1753, 2017
72017
New block bootstrap methods: Sufficient and/or ordered
BH Beyaztas, E Firuzan, U Beyaztas
Communications in Statistics-Simulation and Computation 46 (5), 3942-3951, 2017
62017
Jackknife-after-Bootstrap as logistic regression diagnostic tool
U Beyaztas, A Alin
Communications in Statistics-Simulation and Computation 43 (9), 2047-2060, 2014
62014
New and Fast Block Bootstrap-Based Prediction Intervals for GARCH(1,1) Process with Application to Exchange Rates
BH Beyaztas, U Beyaztas, S Bandyopadhyay, WM Huang
Sankhya Series A 80, 168-194, 2018
52018
Robust BCa–JaB method as a diagnostic tool for linear regression models
U Beyaztas, A Alin, MA Martin
Journal of Applied Statistics 41 (7), 1593-1610, 2014
52014
Forecasting functional time series using weightedlikelihood methodology
U Beyaztas, HL Shang
Journal of Statistical Computation and Simulation, 2019
42019
Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia
SQ Salih, I Alakili, U Beyaztas, S Shahid, ZM Yaseen
Environment, Development and Sustainability 23 (5), 8027-8046, 2021
32021
On Jackknife-After-Bootstrap Method for Dependent Data
U Beyaztas, HB Beyaztas
Computational Economics 53 (4), 1613-1632, 2019
22019
Functional linear models for interval-valued data
U Beyaztas, HL Shang, ASG Abdel-Salam
Communications in Statistics-Simulation and Computation, 2020
12020
Block bootstrap prediction intervals for GARCH processes
B Beste H., U Beyaztas
REVSTAT - Statistical Journal, 2020
12020
Delete‐2 Jackknife‐After‐Bootstrap in Regression
U Beyaztas, A Alin
Quality and Reliability Engineering International 30 (7), 993-1002, 2014
12014
Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model
SK Bhagat, K Pyrgaki, SQ Salih, T Tiyasha, U Beyaztas, S Shahid, ...
Chemosphere 276, 130162, 2021
2021
The system can't perform the operation now. Try again later.
Articles 1–20