Conference: iid2022: Statistical Methods for Event Data - Illuminating the Dynamic Universe

位于亨茨维尔的阿拉巴马大学将于2022年11月组织和主办一个关于天体统计学的讲习班。事件数据在定量科学中很常见，从经济学和社会科学，到物理学、天文学、生物学和其他学科。该研讨会打算将天文学家和其他科学家聚集在一起，举行首次会议，专门讨论整数计数事件数据的统计问题。讲习班的组织形式包括介绍性讲座、投稿陈述和动手练习活动，目的是促进对事件数据统计方法的理解和使用。由国家科学基金会资助的讲习班费用部分主要是为了便利初出茅庐的科学家和代表性不足的少数群体参加和参加。这一讲习班的主要更广泛影响是培训和发展天文学统计数据分析领域的青年科学家，重点是从代表性不足的群体中招募和支助参与者。这次讲习班有两个动机：(A)培训青年科学家并使其参与分析和解释数据的适当统计方法；(B)聚集科学家--包括天文学家和其他相关领域的科学家--交流事件数据统计和分析方面的最新进展。研讨会将涵盖13个主要主题，包括统计估计的一般方法、天文学空间分析的统计、光谱数据的统计、低百分率数据的时间分析的统计以及线性和非线性回归方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The University of Alabama in Huntsville will organize and host a workshop on astrostatistics in November 2022. Event data are common across the quantitative sciences, from economics and social sciences, to physics, astronomy, biology and beyond. The workshop intends to bring together astronomers and other scientists for a first meeting dedicated entirely to statistics for integer–count event data. The workshop is structured in a format that includes introductory lectures, contributed presentations, and hands–on exercise activities, aimed at furthering the understanding and use of statistical methods for event data. The portion of the workshop costs funded by the NSF is primarily intended to facilitate the participation and attendance of early–career scientists and underrepresented minorities. The primary broader impact of this workshop is the training and development of young scientists in the area of statistical data analysis for astronomy, with an emphasis on the recruitment and support of participants from underrepresented groups.This workshop has a two–fold motivation: (a) the training and engagement of young scientists in proper statistical methods for the analysis and interpretation of data; and (b) the gathering of scientists – both astronomers and in other related fields – to exchange recent advances in the statistics and analysis of event data. The workshop will cover thirteen main topics including general methods of statistical estimation, statistics for spatial analysis in astronomy, statistics for spectral data, statistics for temporal analysis of low count-rate data, and linear and non-linear methods of regression.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.