我院预聘-长聘教师王卓博士、助理教授以第一作者身份在统计领域国际顶尖期刊Journal of the American Statistical Association (JASA，美国统计学会会刊)在线发表论文：Towards Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes。该文提出了一类基于边际广义极值分布的加权加和得分函数，并将最佳指纹法(optimal fingerprint)应用于气候极值变化的检测和归因分析中。研究显示，该方法在运算速度、实用性和操作性上都具有稳健的优越性，且存在误差变量时表现更加稳定。
JASA近五年的影响因子(IF)为3.639，是统计领域的国际顶尖期刊。JASA与JRSSB、Annals of Statistics、Biometrika合称统计领域“四大天王”。该文是深圳大学经济学院在JASA期刊上发表的又一篇高水平学术论文，充分彰显我院教师的科研实力。
ABSTRACT: Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location parameters of the marginal generalized extreme value (GEV) distributions of the observed extremes, to a practical and better-understood method. Marginal approachs based on a weighted sum of marginal GEV score equations are promising for no need to specify the dependence structure. The computational efficiency makes them feasible in handling multiple forcings simultaneously. The method under working independence is recommended because it produces robust results where there are errors-in-variables. Our analyses show human influences on temperature extremes at the subcontinential scale. Compared with previous studies, we detected human influences in a slightly smaller number of regions. This is possibly due to the under-coverage of the confidence intervals in existing works, suggeting the need for careful examinations of the properties of the statistical methods in practice.