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Environmental regulations on viral abundance in the upper South China Sea inferred from statistical models Journal article
Hu, Caiqin, Li, Xiangfu, Shi, Zhen, Xu, Jie. Environmental regulations on viral abundance in the upper South China Sea inferred from statistical models[J]. Progress in Oceanography, 2022, 208, 102900.
Authors:  Hu, Caiqin;  Li, Xiangfu;  Shi, Zhen;  Xu, Jie
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:3.8/4.0 | Submit date：2022/12/01
Environmental Factors  Generalized Boosted Model  Statistical Models  The South China Sea  Viral Abundance  
Estimation of spatiotemporal response of rooted soil using a machine learning approach 基于机器学习算法估算根系土体特性的时空响应 Journal article
Cheng,Zhi liang, Zhou,Wan huan, Ding,Zhi, Guo,Yong xing. Estimation of spatiotemporal response of rooted soil using a machine learning approach 基于机器学习算法估算根系土体特性的时空响应[J]. Journal of Zhejiang University: Science A, 2020, 21(6), 462-477.
Authors:  Cheng,Zhi liang;  Zhou,Wan huan;  Ding,Zhi;  Guo,Yong xing
Favorite | TC[WOS]:16 TC[Scopus]:15  IF:3.3/2.9 | Submit date：2021/03/11
Genetic Programming (Gp)  Simplified Statistical Model  Soil Suction  Spatiotemporal Variations  Tu413.7  
Predicting ground-level ozone concentrations by adaptive Bayesian model averaging of statistical seasonal models Journal article
K. M. Mok, K. V. Yuen, K. I. Hoi, K. M. Chao, D. Lopes. Predicting ground-level ozone concentrations by adaptive Bayesian model averaging of statistical seasonal models[J]. Stochastic Environmental Research and Risk Assessment, 2017, 32(5), 1283-1297.
Authors:  K. M. Mok;  K. V. Yuen;  K. I. Hoi;  K. M. Chao;  D. Lopes
Favorite | TC[WOS]:10 TC[Scopus]:12  IF:3.9/3.6 | Submit date：2018/10/30
Adaptive Bayesian Model Averaging  Kalman Filter  Model Switching  Ozone Prediction  Statistical Model  
Random-effects models for meta-analytic structural equation modeling: review, issues, and illustrations Journal article
Cheung,Mike W.L., Cheung,Shu Fai. Random-effects models for meta-analytic structural equation modeling: review, issues, and illustrations[J]. Research Synthesis Methods, 2016, 7(2), 140-155.
Authors:  Cheung,Mike W.L.;  Cheung,Shu Fai
Favorite | TC[WOS]:86 TC[Scopus]:82 | Submit date：2019/06/25
Meta-analysis  Meta-analytic Structural Equation Model  r Statistical Platform  Random-effects Model  Structural Equation Model  
iCPE: A hybrid data selection model for SMT domain adaptation Conference paper
Wang L., Wong D.F., Chao L.S., Lu Y., Xing J.. iCPE: A hybrid data selection model for SMT domain adaptation[C]:SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2013, 280-290.
Authors:  Wang L.;  Wong D.F.;  Chao L.S.;  Lu Y.;  Xing J.
Favorite | TC[WOS]:1 TC[Scopus]:4 | Submit date：2018/12/24
Data Selection  Domain Adaptation  Hybrid Model  Similarity Metrics  Statistical Machine Translation  
Korean-Chinese statistical translation model Conference paper
Li S., Wong D.F., Chao L.S.. Korean-Chinese statistical translation model[C], 2012, 767-772.
Authors:  Li S.;  Wong D.F.;  Chao L.S.
Favorite | TC[Scopus]:4 | Submit date：2018/12/24
Factored Translation Model  Korean-chinese  Statistical Machine Translation  
Modelling the Daily Maximum of the 8-hr Averaged Ozone Concentrations in Macau with Dynamic Statistical Models Conference paper
Mok, K. M., Hoi, K. I., Yuen, K. V., Chao, K. M.. Modelling the Daily Maximum of the 8-hr Averaged Ozone Concentrations in Macau with Dynamic Statistical Models[C], Utrecht, 2012, 1-4.
Authors:  Mok, K. M.;  Hoi, K. I.;  Yuen, K. V.;  Chao, K. M.
Favorite |  | Submit date：2022/07/27
Dynamic statistical model  Macau  Tropospheric ozone  
Iterative Probabilistic Approach for Selection of Time-varying Model Classes Journal article
Hoi, K. I., Yuen, K. V., Mok, K. M.. Iterative Probabilistic Approach for Selection of Time-varying Model Classes[J]. Procedia Engineering, 2011, 2585-2592.
Authors:  Hoi, K. I.;  Yuen, K. V.;  Mok, K. M.
Favorite | TC[WOS]:1 TC[Scopus]:1 | Submit date：2022/07/27
Bayesian  Kalman Filter  Model Class Selection  Statistical Model  
Iterative Probabilistic Approach for Selection of TimeVarying Model Classes Conference paper
K.I. Hoi, K.V. Yuen, K.M. Mok. Iterative Probabilistic Approach for Selection of TimeVarying Model Classes[C]:ELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, 2011, 2585-2592.
Authors:  K.I. Hoi;  K.V. Yuen;  K.M. Mok
Favorite | TC[WOS]:1 TC[Scopus]:1 | Submit date：2019/02/12
Bayesian  Kalman Filter  Model Class Selection  Statistical Model  
Research of predicting machine's remaining useful life based on statistical pattern recognition and auto-regressive and moving average model Journal article
Liao W.-Z., Pan E.-S., Wang Y., Xi L.-F.. Research of predicting machine's remaining useful life based on statistical pattern recognition and auto-regressive and moving average model[J]. Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2011, 45(7), 1000-1005.
Authors:  Liao W.-Z.;  Pan E.-S.;  Wang Y.;  Xi L.-F.
Favorite |  | Submit date：2019/01/16
Auto-regressive and moving average (ARMA) model  Health index (HI)  Prediction  Remaining useful life (RUL)  Statistical pattern recognition (SPR)