活動起日：2017-04-18 發佈日期：2017-04-18 瀏覽數：256 2017-04-18 更新
國立臺灣大學計量理論與應用研究中心 (CRETA)、國立臺灣大學財務金融學系及臺灣經濟計量學會 (TES) 將共同舉辦 4 月份WETA @TES。4 月份 WETA 研討會相關資訊如下。
【2017 年 4 月份 WETA 研討會】
日期：2017 年 4 月 28 日 (週五) 下午2:00~5:00
地點：國立臺灣大學管理學院二號館三樓 304 教室
(1) Estimating Links of a Network from Time to Event Data
(2) Solving Fused Group Lasso Problems via Block Splitting Algorithms
In this paper we develop a statistical method for identifying links of a network from time to event data. This method models the hazard function of a node conditional on event time of other nodes, parameterizing the conditional hazard function with the links of the network. It then estimates the hazard function by maximizing a pseudo partial likelihood function with parameters subject to a user-specified penalty function and additional constraints. To make such estimation robust, it adopts a pre-specified risk control on the number of false discovered links by using the Stability Selection method. Simulation study shows that under this hybrid procedure, the number of false discovered links is tightly controlled while the true links are well recovered. We apply our method to estimate a political cohesion network that drives donation behavior of 146 firms from the data collected during the 2008 Taiwanese legislative election. The results show that firms affiliated with elite organizations or firms of monopoly are more likely to diffuse donation behavior. In contrast, firms belonging to technology industry are more likely to act independently on donation.
Keywords: Hazard network models; Right-censored data; Partial likelihood function; Stability Selection; Political cohesion networks.
In this paper we propose a distributed optimization-based method for solving the fused group lasso problem, in which the penalty function is a sum of Euclidean distances between pairs of parameter vectors. As a result of that, the penalty function is not separable in terms of these parameter vectors. To make the penalty function separable, one common way is to introduce a set of auxiliary variables that represent the differences between pairs of parameter vectors. This representation can be seen as a linear operator on the joint vector of the parameter vectors, and the resulting augmented Lagrangian will have a coupling quadratic term involving the linear representation. Even though the linear representation is separable in terms of the parameter vectors, the coupling quadratic term is not. To make the coupling quadratic term separable, we further introduce a set of equality constraints that connect each parameter vector to a group of paired auxiliary variables. With these newly introduced equality constraints, we are able to derive a modified augmented Lagrangian that is separable either in terms of the parameter vectors or in terms of the paired auxiliary variables. This separable property further facilitates us to solve the fused group lasso problem by developing an iterative algorithm with that most tasks can be carried out independently in parallel. We evaluate performance of the parallel algorithm by carrying out fused group lasso estimation for regression models using simulated data sets. Our results show that the parallel algorithm has a massive advantage over its non-parallel counterpart in terms of computational time and memory usage. In addition, with additional steps in each iteration, the parallel algorithm can obtain parameter values almost identical to those obtained by the non-parallel algorithm.
Keywords: Fused lasso; Group lasso; Scalability; Alternating direction method of multipliers; Block splitting algorithms.
下午 1：30 ~ 2：00報到
下午 2：00 ~ 3：20 First session
下午 3：20 ~ 3：40 Tea Break
下午 3：40 ~ 5：00 Second session
報名期限：4/27 (四) 13:30
為方便臺灣經濟計量學會 (TES) 會員繳納 106 年度會費，本次活動開放現場繳納會費，亦歡迎大家介紹非會員朋友加入 TES。更多研討會資訊請見 TES 網站：http://www.tesociety.org.tw/main.php。