Conference on Predictive Inference and Its Applications

爱荷华州立大学统计学系将于2018年5月7日至8日在爱荷华州艾姆斯举办预测推理及其应用会议。会议计划包括16位杰出演讲者的全体会议报告和学生，博士后学者和早期职业研究人员的海报会议报告。会议的目标包括提高人们对预测重要性的认识，展示该领域当前和新兴领导者的研究成果，推动开发更准确的预测方法，并鼓励具有互补技能和能力的不同科学家之间的互动与合作。除了吸引统计博士和统计专业的研究生外，会议还有可能吸引更广泛的数据科学社区的参与，包括来自生物信息学、商业分析、计算机科学、电气和计算机工程、经济学和社会科学等领域的研究人员。预测推理及其应用会议将为开发新方法提供一个有价值的场所，这些方法可以在我们这个数据丰富的世界中准确预测和评估不确定性。这些方法提供了有价值的见解，并导致科学和工业更好的决策。在现代世界中，涉及预测未观察到但最终可观察到的量的问题无处不在。从历史上看，统计学科更强调假设检验和参数估计，而不是预测。其他与统计学相关但不等同于统计学的研究团体，包括计算机科学、机器学习、分析学、大数据、数据挖掘和数据科学，已经更全面地接受了预测问题，并开发了各种有用的预测工具。尽管如此，统计思维和创新在解决现在和未来的预测问题方面发挥着重要作用。在某些情况下，可以使用统计思维来生成不确定性的适当度量，以配合现有工具提供的点预测。在其他情况下，统计思维可以导致预测推理的方法，在偏差和方差之间取得更好的平衡，或者比现有方法更有效地利用数据。会议讨论中产生的正式报告和想法旨在推动科学进步，并产生将出现在评审期刊文章中的出版物。这项研究将提高预测方法的技术水平，这反过来将导致许多领域的进步，在这些领域中，准确的预测和准确的预测误差评估是必不可少的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Department of Statistics at Iowa State University will host the Conference on Predictive Inference and Its Applications on May 7 and 8, 2018, in Ames, Iowa. The conference program includes plenary presentations from 16 distinguished speakers and a poster session featuring presentations from students, postdoctoral scholars, and early-career researchers. Goals of the conference include raising awareness about the importance of prediction, showcasing research of current and emerging leaders in the field, motivating the development of more accurate prediction methods, and encouraging interactions and collaborations among a diverse collection of scientists with complementary skills and abilities. In addition to attracting PhD statisticians and graduate students majoring in statistics, the conference has the potential to draw participation from the broader data science community that includes researchers from bioinformatics, business analytics, computer science, electrical and computer engineering, economics, and social sciences among other areas. The Conference on Predictive Inference and Its Applications will provide a valuable venue for developing new methodologies that enable accurate prediction and assessment of uncertainty in our data-rich world. Such methodologies provide valuable insights and lead to better decision making in science and industry.Problems involving the prediction of unobserved but eventually observable quantities are ubiquitous in the modern world. Historically, the discipline of statistics has placed greater emphasis on hypothesis testing and parameter estimation than it has on prediction. Other research communities connected to but not equivalent to statistics, including computer science, machine learning, analytics, big data, data mining, and data science have more fully embraced prediction problems and developed a variety of useful prediction tools. Nonetheless, statistical thinking and innovation have an important role to play in addressing prediction problems now and in the future. In some cases, statistical thinking can be used to generate appropriate measures of uncertainty to accompany point predictions provided by existing tools. In other cases, statistical thinking can lead to methods for predictive inference that strike a better balance between bias and variance or make more efficient use of the data than existing approaches. The formal presentations and ideas that arise from discussions at the conference are intended to motivate scientific progress and result in publications that will appear in refereed journal articles. This research will enhance the state of the art in prediction methodology, which in turn will lead to advances in many fields where accurate predictions and accurate assessments of prediction error are essential. The conference website is available at PredictiveInference.github.io.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.