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Membership and affiliation

Currently:

Fellow of the Royal Statistics Society (www.rss.org.uk) 

Associate Editor of Journal of the American Statistical Association A&CS (JASA) , 2022---

Associate Editor of  Journal of the Royal Statistical Society series A (JRSS-A), 2018—2022

Associate Editor of Statistics and Its Interface, 2016—

Associate Editor of Journal of Statistics THeory and Practice (JSTP), 2018---

• Listed as a Top 2% Scientist of the World  (Stanford/Elsevier, single-year data sets) from 2020---.
• :
• Google Scholar
• The paper of Bayesian Quantile Regression has been lsited as the `Most Downloaded Statistics & Probability Letters Articles'  since the publication in 2001:

The impact of some of my papers goes out the academic community now as three leading software:

The GAUSS package Qreg (http://www.thierry-roncalli.com/download/gauss-qreg.pdf)  implemented the methods of Yu and Jones (1998)’;

The STATSA package () directly detailed my Bayesian model and method of Yu and Moyeed (2001). 

The SAS package () also implement my Bayesian Quantile Regression. 

Industry Consulting Experience

1.Case description of Application of DCVG-survey to predict coating defect size on pipelines (2014-2018):

Corrosion activity is hard to model using indirect inspection techniques. For the underground crude oil pipelines operated by NISOC in Iran, in order to identify coating damage, DCVG was performed along these pipelines. Data from pre-assessment, indirect inspection and direct examination (the first three steps of ECDA as specified by NACE) was gathered and analysed. Proper analysis tools and software were produced for companies via TWI (). 

Reference:  (1) Noor, N., Yu, K., U. Bharadwaj, and T-H.Gan (2018)  Making Use of External Corrosion Defect Assessment (ECDA) Data to Predict DCVG %IR Drop and Coating Defect Area, Materials and Corrosion, 69, 1237-1256.  (2) Anes-Arteche, F., Yu, K.; Bharadwaj, U, Wang, B. and Lee, C. (2017) Challenges in the application of DCVG-survey to predict coating defect size on pipelines, Materials and Corrosion, 68, 339-337.

2 .  Case description of Weibull analysis and extreme value analysis of small data from cable failures  small date provided by the National Grid Electricity Transmission Plc and ScottishPower Transmission Limited and the NGET/SPT Upgrades Limited (2019). 

References in method:  (1) Yu, K., Wang, B. and Patilea, V. (2013) New estimating equation approaches with application in lifetime data analysis, Annals of the Institute of Statistical Mathematics. (2) Wang, B., Yu, K. and Jones, M.C. (2010) Inference under progressively Type II right censored sampling for certain lifetime distributions, Technometrics, 52, 453–460.  (3) He, J., Sheng, Z., Wang, B. and Yu, K. (2014) Point and exact interval estimation for the generalized Pareto distribution with small samples, Statistics and Its Interface. (4) Wang, B.X., Yu, K. and Sheng, Z. (2014) New Inference for Constant-Stress ALT with Weibull Distribution and Progressively Type II Censoring, IEEE Transactions on RELIABILITY, 63, 3.

3. Case description: to help 5one () (2013-2014) develop their methods in Transferred Demand to  look at impact on individual product sales: Transferred Demand as defined is the prediction of the movement and substitution of sales from product to product when changes in assortment are made to a category within a grocery retailer (due to a lack of availability or de-listing of a product).

4. Case description: to address the question in health and biomedical science: does parental obesity affect children's obesity? data analysis from Iran and Australia. Also support by NIHR () (2012-2013, 2020).

References in method:  (1)   Yu, K. Liu, X. Alhamzaw, R., Becker, F. and Lord, J. (2018) Statistical methods for body mass index: a selective review, Statistical Methods in Medical Research, 27(3) 798–811. (2) Ardalan A. and Yu, K. (2020) Bayesian quantile regression analysis of parental obesity e ect on children's obesity in Iran. (3) Peluso, A., Vinciotti, V. and Yu, K. (2019), Discrete Weibull generalised additive model: an application to count fertility data, Journal of the Royal Statistical Society (Applied Statistics), C., 68, Part 3, pp.565–583.

5. Case description: to help OptiRisk () witt financial risk assessment from the analysis of marketing data and sentiment data also funded by 

References in method:  (1) Xu, Q., Chen, L., Jiang, C. and Yu, K. (2020),   Mixed data sampling expectile regression with applications to measuring financial risk, Economic Modelling, 91, 469-481. (2) Jiang, R., Hu, X. and Yu, K. (2020), Single-Index Expectile Models for Estimating Conditional Value at Risk and Expected Shortfall, Journal of Financial Econometrics, in press. (3) Taylor, J.W. and Yu, K. (2016) Using Autoregressive Logit Models to Forecast the Exceedance Probability for Financial Risk Management,  Journal of the Royal Statistical Society-A,  179, 1069-1092.  (4)  Hand, D.J. and Yu, K. (2009), Justifying adverse actions with new scorecard technologies, The Journal of Financial Transformation, Vol. 26, page 13—17. (5)  Wu, W., Yu, K. and Mitra, G.(2008), Kernel Conditional Quantile Estimation for Stationary Processes with Application to Conditional Value-at-Risk, Journal of Financial Econometrics, 6(2), 253-270.