主题一：Signal Lasso with Non-Convex Penalties for Efficient Network Reconstruction in Complex System
主讲人：云南财经大学统计学科石磊教授
主持人：统计与数据科学学院林华珍教授
时间：2026年10月22日（周四）下午13:00-14:00
地点：柳林校区弘远楼408会议室
主办单位：统计与数据科学学院和统计研究中心 科研处
主讲人简介：
石磊，教授，博士生导师；教育部重大人才支持计划，国家高层次领军人才计划入选者。现任云南财经大学统计学科首席教授，云南同创科学计算与数据挖掘研究中心主任，上海立信会计金融学院统计与数学学院特聘院长；担任中国现场统计研究会副理事长、中国数量经济学会副会长；中国现场统计研究会统计交叉科学研究分会理事长。全国应用统计专业学位研究生教育指导委员会委员，国家社会科学基金学科规划评审组专家。在国内外学术期刊PNAS（美国国家科学院院刊），Nature Communication，Nature Human Behaviour，Science Advances, National Science Review，Biometrika，Physical Review Research,《中国科学》等杂志发表论文200余篇；曾获第七届霍英东高校优秀青年教师一等奖; 教育部自然科学二等奖，省级自然科学一、二等奖、科技进步一等奖，国家统计局优秀教师奖、科技进步奖，中国数量经济学杰出学者奖等40余项奖励。
内容提要：
Inferring network structures remains an interesting question due to its importance in understanding and controlling the collective dynamics of complex systems. Most real networks exhibit sparsely connected properties, and the connection parameter is a signal connected (0 or 1). Existing shrinking methods such as Lasso-type estimation can not suitably reveal such property. An alternative method, called signal lasso (or its updating version: adaptive signal lasso), was recently proposed to solve the network reconstruction, where the signal parameter can be shrunk to either 0 or 1 in two different directions, and they found that signal lasso outperformed the lasso-type method. The signal lasso or adaptive signal lasso employed the additive penalty of signal and non-signal terms which is a convex function and easily to complementation in computation. However, their methods need tuning the one or two parameters to find an optimal solution, which is time cost for large size network. In this paper, we propose a new signal lasso method based on two penalty functions to estimate the signal parameter and uncover the network topology in a complex network with a small number of observations. The penalty functions we introduced are non-convex functions, thus coordinate descent algorithms are suggested. We find in this method that the tuning parameter can be set to a large enough value such that the signal parameter can be completely shrunk either 0 or 1. The extensive simulations are conducted in linear regression models with different assumptions, the evolutionary-game-based dynamic model, and Kuramoto model of the synchronization problem. The advantages and disadvantages of each method are fully discussed under various conditions. Finally, a real example comes from a behavioral experiment is detailed analyzed. Our results show that signal lasso with non-convex penalties is effective and fast in estimating signal parameters in the linear regression model.
