公司统计学系姜云卢老师的论文“An exponential-squared estimator in the autoregressive model with heavy-tailed errors ”在《Statistics and Its Interface》期刊上发表。此期刊为统计学领域的一流期刊，在JCR分区中，属于2区，2014年，其影响因子为2.933，在所有出版原创论文的统计期刊中排第二（Statistics and Its Interface achieved an ISI Web of Knowledge citation impact of 2.933 for calendar year 2014 — the second highest among all statistical journals that publish original articles.）。
该文摘要为：In this paper, an exponential-squared estimator is introduced in the autoregressive model with heavy-tailed errors. Under some conditions, the -consistency of the proposed estimator is established. Since the exponential-squared estimator involves a tuning parameter, we select via a five-fold cross validation procedure. Simulation studies illustrate that the finite sample performance of proposed method performs better than that of a self-weighted composite quantile regression (SWCQR) method and self-weighted least absolute deviation (SWLAD) method in terms of Sd and MSE when the error follows a heavy-tailed distribution and there are outliers in the dataset. Finally, we apply the proposed methodology to analyze the Recruitment series.
姜云卢老师致力于稳健统计与高维数据分析等方面的研究，已发表10多篇SCI收录论文，并主持国家自科青年项目1项。
