| 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 | 89 | 2020 |
| 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 | 76 | 2021 |
| 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 | 46 | 2021 |
| 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 | 41 | 2019 |
| Drought interval simulation using functional data analysis U Beyaztas, ZM Yaseen Journal of Hydrology 579, 124141, 2019 | 35 | 2019 |
| 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 | 35 | 2018 |
| 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 | 25 | 2021 |
| On function-on-function regression: Partial least squares approach U Beyaztas, HL Shang Environmental and Ecological Statistics, 2020 | 24 | 2020 |
| Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models U Beyaztas, A Alin Statistical Papers 55, 1001-1018, 2014 | 18 | 2014 |
| A robust functional partial least squares for scalar‐on‐multiple‐function regression U Beyaztas, H Lin Shang Journal of Chemometrics 36 (4), e3394, 2022 | 15 | 2022 |
| Forecasting functional time series using weightedlikelihood methodology U Beyaztas, HL Shang Journal of Statistical Computation and Simulation, 2019 | 13 | 2019 |
| Functional linear models for interval-valued data U Beyaztas, HL Shang, ASG Abdel-Salam Communications in Statistics-Simulation and Computation, 2020 | 12 | 2020 |
| A partial least squares approach for function-on-function interaction regression U Beyaztas, HL Shang Computational Statistics, 1-29, 2021 | 11 | 2021 |
| 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 | 11 | 2013 |
| 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 | 10 | 2017 |
| 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 | 10 | 2017 |
| 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 | 8 | 2021 |
| Jackknife-after-bootstrap as logistic regression diagnostic tool U Beyaztas, A Alin Communications in Statistics-Simulation and Computation 43 (9), 2047-2060, 2014 | 8 | 2014 |
| 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, 2022 | 7 | 2022 |
| A comparison of parameter estimation in function-on-function regression U Beyaztas, HL Shang Communications in Statistics-Simulation and Computation 51 (8), 4607-4637, 2022 | 6 | 2022 |
Han Lin ShangDepartment of Actuarial Studies and Business Analytics, Macquarie UniversityVerified email at mq.edu.au
