Takip et
Ufuk Beyaztas
Ufuk Beyaztas
Department of Statistics, Marmara University
marmara.edu.tr üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
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
1062020
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
902021
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
582021
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
472019
Drought interval simulation using functional data analysis
U Beyaztas, ZM Yaseen
Journal of Hydrology 579, 124141, 2019
392019
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
362018
On function-on-function regression: Partial least squares approach
U Beyaztas, HL Shang
Environmental and Ecological Statistics, 2020
352020
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
312021
Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models
U Beyaztas, A Alin
Statistical Papers 55, 1001-1018, 2014
202014
A robust functional partial least squares for scalar‐on‐multiple‐function regression
U Beyaztas, H Lin Shang
Journal of Chemometrics 36 (4), e3394, 2022
192022
Forecasting functional time series using weightedlikelihood methodology
U Beyaztas, HL Shang
Journal of Statistical Computation and Simulation, 2019
172019
Functional linear models for interval-valued data
U Beyaztas, HL Shang, ASG Abdel-Salam
Communications in Statistics-Simulation and Computation, 2020
142020
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
142017
A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction
U Beyaztas, HL Shang, ZM Yaseen
Journal of Hydrology 598, 126380, 2021
132021
Establishment of Dynamic Evolving Neural‐Fuzzy Inference System Model for Natural Air Temperature Prediction
SK Bhagat, T Tiyasha, Z Al-Khafaji, P Laux, AA Ewees, TA Rashid, S Salih, ...
Complexity 2022 (1), 1047309, 2022
122022
A partial least squares approach for function-on-function interaction regression
U Beyaztas, HL Shang
Computational Statistics, 1-29, 2021
122021
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
122013
Function-on-function partial quantile regression
U Beyaztas, HL Shang, A Alin
Journal of Agricultural, Biological and Environmental Statistics 27 (1), 149-174, 2022
112022
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
112017
Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models
U Beyaztas, HL Shang
Journal of Applied Statistics 49 (5), 1179-1202, 2022
102022
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Makaleler 1–20