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讲者名单及讲题

陆方研讨会会议手册

开幕演讲人:黄文璋 (高雄大学退休教授)
题目:海峡两岸机率统计交流25年
摘要
25年前,1996年,在两岸机率与统计界齐心合作下,第一届海峡两岸统计学研讨会,在位于台湾高雄的中山大学举行。会议圆满成功,并开启了两岸机率统计界的交流活动。之后,陆续有两岸大学统计相关系所的互访,及联合举行会议等。而海峡两岸机率与统计学术研讨会的第二届、第三届、…,轮流在两岸各地举行,至今已有十一届。其间虽有若干届因故延后举行,惟在两岸机率与统计界,皆具高度诚意下,本研讨会一直持续不断。25年来,海峡两岸会议对加强双方机率统计之互动,及促进彼此了解,发挥极大功能。在本演讲中,我们将回顾四分之一世纪前,举办第一届海峡两岸统计学研讨会之经过,并略述其后几届的举办情形。我们也会对未来两岸机率与统计之交流,提出若干建议。
经历
  • 高雄大学副校长(2004年8月31日~2006年10月31日)
  • 中华机率统计学会理事长(2001年4月~2004年4月)
  • 主办第一届海峡两岸统计学研讨会(1996.7.15~16) 
  • 第二届海峡两岸统计与概率学术研讨会(苏州,1999.7.24~25)组织委员会委员
  • 第三届海峡两岸统计与概率学术研讨会暨2001中华机率统计学会学术研讨会学术委员会委员
  • 第五届泛华统计协会国际会议(香港,2001.8.17~19)荣誉谘询委员会委员
  • 第四届海峡两岸统计与概率学术研讨会(兰州,2004 8.16~18)组织委员会委员
  • 第五届海峡两岸统计与概率学术研讨会(新竹国家卫生研究院,2006.7.29~30)筹备委员会委员
  • 第七届海峡两岸机率与统计研讨会暨2010年中华机率统计学会及学术研讨会(花莲东华大学,2010.5.1~2)议程委员会委员
  • 第八届海峡两岸机率与统计研讨会(大陆哈尔滨,2012.8.14~16)筹备委员会委员
  • 第九届海峡两岸机率与统计学术研讨会(台中逢甲大学、国立中兴大学,2014.5.16~18)学术指导委员会委员
有关黄教授其他的重要学术成就请参考以下网站:
http://www.stat.nuk.edu.tw/huangwj/cindex.htm

Keynote Speaker: 卢鸿兴 (阳明交通大学统计研究所教授)
题目:人工智能辅助门诊的统计学习
摘要
这项研究报告了与台北荣民总医院联合开发的AI辅助门诊。针对特定的临床应用,讨论了通过医学图像的多种模式使用深度学习技术的计算机辅助诊断系统的设计。研究了相关问题、以集成统计模型、计算算法和领域知识。 总结了当前的发展,并提出了未来的潜在研究。
经历
卢鸿兴教授学术成就卓越, 主要研究领域包括: Statistics, Imagine Science, Bioinformatics, Data Science, Machine Learning and Scientific Computation, 并有众多论文发表在相关领域的顶尖期刊, 例如: JASA, Statistica Sinica, JCGS, Bioinformatics, IEEE Trans. Medical Image, Biostatistics, PLoS ONE, etc. 卢教授担任过许多重要期刊的(副)编辑 (如: WIREs Computational Statistics, JASA, J. Applied Mathematics, Statistica Sinica, Journal of Data Science, Handbook of Statistical Bioinformatics, International Journal of Systems and Synthetic Biology, etc), 也是许多重要学术组织的荣誉会员, 并曾经担任过许多重要的行政职位 (如: Elected member of ISI, Principal fellow of Higher Education Academy (PFHEA), Board of Directors for ICSA, Vice President for NCTU Academic Affairs, Dean for NCTU College of Science, Director of NCTU Big Data Research Center, etc).

有关卢教授其他的重要学术成就请参考以下网站:
https://www.stat.nctu.edu.tw/zh_cn/members/teacher/-%E7%9B%A7%E9%B4%BB%E8%88%88-58192428

 
Session 1-5演讲者 (依场次安排)
Session 1 (时间序列及应用:主持人:黄子铭副教授)
演讲人1陈婉淑 (逢甲大学统计系)
题目Bayesian estimation of realized GARCH-type models with application to financial tail risk management
摘要
Advances in the various realized GARCH models have proven effective in taking account of the bias in realized volatility (RV) introduced by microstructure noise and non-trading hours. They have been extended into nonlinear or long-memory patterns, including the realized exponential GARCH (EGARCH), realized heterogeneous autoregressive GARCH (HAR-GARCH), and realized threshold GARCH (TGARCH) models. These models with skew Student's t-distribution are applied to quantile forecasts such as Value-at-Risk and expected shortfall of financial returns as well as volatility forecasting. Parameter estimation and quantile forecasting are built on Bayesian Markov chain Monte Carlo sampling methods. Backtesting measures are presented for both Value-at-Risk and expected shortfall forecasts and employ two loss functions to assess volatility forecasts. Results taken from the S&P500 in the U.S. market with approximately 5-year out-of-sample periods covering the COVID-19 pandemic period are reported as follows: (1) The realized HAR-GARCH model performs best in respect of violation rates and expected shortfall at the 1% and 5% significance levels. (2) The realized EGARCH model performs best with regard to volatility forecasts.

演讲人2黄士峰 (高雄大学统计研究所)
题目 A Network Autoregressive Model with GARCH Effects and its Applications
摘要
This study proposes a network autoregressive model with GARCH effects, denoted by NAR-GARCH, to depict the return dynamics of stock market indices. A GARCH filter is employed to remove the GARCH effects of each index marginally and the NAR model with the Granger causality test and Pearson's correlation test with sharp price movements is used to capture the joint effects caused by other indices with the most updated market information. The NAR-GARCH model is designed to depict the joint effects of multidimensional time series in an easy-to-implement and effective way. The returns of 20 global stock indices from 2006 to 2020 are employed for our empirical investigation. The numerical results reveal that the NAR-GARCH model has satisfactory performances in both fitting and prediction for the 20 stock indices, especially when a market index has strong upward or downward movements.

演讲人3林良靖 (成功大学统计系)
题目 Symbolic interval-valued time series models
摘要
This study considers interval-valued time series data. To characterize such data, we propose an auto-interval-regressive moving average (AIRMA) model using the order statistics from normal distributions. Furthermore, to better capture the heteroscedasticity in volatility, we design a generalized heteroscedastic volatility AIR (GHVAIR) model. We derive the likelihood functions of the AIRMA and GHVAIR models to obtain the maximum likelihood estimator. Monte Carlo simulations are then conducted to evaluate our methods of estimation and confirm their validity. A real data example from the PM2.5 levels on south Taiwan is used to demonstrate our method.

演讲人4郑宇翔 (世新大学财务金融学系)
题目非线性因果关系检定
摘要
因果关系检定是一种在经济及财务领域中用来检查数列关系的方法,其中最常被使用的为线性架构的Granger因果关系检定。为了发现数列间可能存在的更一般性之因果关系,许多非线性因果关系检定已被提出。在此演讲中将考虑基于B-spline架构下的Granger因果关系,并探讨模型中参数设定之议题,如B-spline节点的选取等问题。最后此研究将使用数种不同的因果关系检定来分析多国股价之间的因果关系。
 
Session 2(空间统计与存活分析;主持人:黄佳慧副教授)
演讲人1陈春树 (中央统研所)
题目Model selection with an anisotropic nested spatial correlation structure
摘要
In spatial regression analysis, a suitable specification of the mean regression model is crucial for unbiased analysis. Suitably account for the underlying spatial correlation structure of the response variables is also an important issue. Here, we focus on selection of an appropriate mean model in spatial regression analysis under a general anisotropic nested spatial correlation structure. We propose a distribution-free model selection criterion which is an estimate of the weighted mean squared error based on assumptions only for the first two moments of the response data. The simulations under the settings of covariate selection reveal that the proposed criterion performs well for covariate selection in the mean model regardless of the underlying spatial correlation structure is nested, non-nested, isotropic, or anisotropic. Also, the proposed criterion accommodates both continuous and count response data. Finally, a real data example regarding the fine particulate matter concentration is also analyzed for illustration.

演讲人2杨洪鼎 (高雄大学统计研究所)
题目A predicting perspective variable selection for the spatial regression model under the presence of spatial confounding
摘要
The spatial regression model has been wildly used in analyzing spatially referenced data, which includes observed covariates and unobserved spatial random effects. When spatial confounding exists, it has been pointed out that parameter estimation and spatial prediction are unreliable. To overcome the issue, we propose a procedure of selecting covariates for spatial regression. We first introduce an adjusted estimation method of regression coefficients and the consequent spatial predictor. Then, we discover a generalized conditional Akaike information criterion to select a subset of covariates, resulting in variable selection and spatial prediction that are satisfactory. Statistical inferences of the proposed methodology are justified theoretically and numerically. This is a joint work with Yung-Huei Chiou and Chun-Shu Chen.

演讲人3黄佳慧 (政治大学统计系)
题目Joint Analysis of Dependent Count Data with Excessive Zero
摘要
This work is motivated by an obstructive sleep apnea study in Taiwan, where patients may experience breathing stops for brief periods of time during sleep. Individuals need to take one night at the laboratory and polysomnography (PSG)  is used to measure their sleep variables, such as apnea-hypopnea index (AHI) or respiratory disturbance index (RDI). With this sleep study, there were two types of sleep positions recorded and some observed lateral RDI had a substantial portion of zeros. To this aim, we propose a Bayesian framework to model marginal distribution of event rates and use copula functions to analyze the dependent structure between the sleeping position and position-specific RDI. We develop estimating equations for the model parameters via marginal approach and EM algorithm to obtain estimate of the association parameter. Simulations are conducted to demonstrate the finite sample properties of the proposed analysis, and a real application is reported to illustrate its utility.

演讲人4陈政辉 (政治大学应用数学系)
题目Modeling chronic HBV infections with survival probability metrics
摘要
慢性病的病程常以Markov过程建模。由于慢性病的病程很长,通常很难只由一个临床研究观察到整个过程,因此,模型参数多是基于不同的短期临床研究整合得出。尽管这种整合方法为慢性病的病程提供了基本全貌,但在深入分析下可能会导致不切实际的结果。例如,如果没有仔细校准,模型计算出的患者寿命可能比一般人还长。这样的结果通常是由于自然死亡率的影响没有很好地被这些短期研究考虑。学者Beck Pauker 提出一种将自然死亡率整合到病程模型的方法。然而,他们的方法隐含了自然死亡率仅影响转变到死亡状态和保持在初始状态两路径的假设,未解释为什么只有这两种类型的路径会受到自然死亡率的影响。从实际情况来看,无论患者经历何种状态转变,都会面临自然死亡风险。基于这一观察,我们提出了一种新的整合方法。假设自然死亡率独立地影响Markov模型中所有状态转换的路径。这扩展了 Beck Pauker 的想法,也使他们的方法更加合理。我们提出的方法被用于考虑慢性B型肝炎病毒 (HBV) 感染的病程分析。数值结果表明,此方法可以计计算患者的预期寿命与生存概率。此外,首次事件发生时间(first hitting time)的概念被用以推导患者在指定时间内会经历危急医疗状态的概率,提供了慢性B型肝炎患者有价值的医疗讯息。我们也讨论该方法的可能扩展,例如,所得模型在不同国家的应用以及如何将患者的风险因子引入模型,提供进一步的研究方向。
 
Session 3(生物资讯与应用统计;主持人:吴汉铭副教授)
演讲人1叶倚任 (高雄师范大学数学系)
题目Arteriovenous Graft Failure Detection in Hemodialysis
摘要
The fast-growing prevalence of end stage renal disease (ESRD) leads to an increasing burden of population requiring dialysis worldwide. Specifically, patients who choose to receive hemodialysis (HD) face the problem of maintaining their arteriovenous accesses. Unfortunately, occurrence of stenosis and clots is not uncommon, especially in arteriovenous grafts (AVG). Blood flow through grafts contains a number of information that can help prevent these circumstances and improve graft longevity. The aim of this study is to develop a portable recording device that detects stenosis by extracting information from blood flow sounds.

演讲人2苏家玉 (台北医学大学医学资讯研究所)
题目An Infodemiology Study of Internet Search Trends in Spatially Clustered COVID-19 Areas in the United States
摘要
Objective: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. Hence, this study identified COVID-19 clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas in the United States.
Methods: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman’s rank correlation coefficients and Poisson regression models for period of January to December 2020.
Results: Huge clusters involving multiple states were found, which resulted from varied control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performances was found among states and time periods, possibly suggesting the necessity of utilizing different frameworks for Google RSV data in each state. Moreover, the sign of a correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk.
 
演讲人3马弥嘉 (成功大学统计系)
题目发现重要代谢物数据特征的视觉化统计方法
摘要
本研究目的是使用统计学的视觉化方法发现代谢物数据有显着差异的特征,以确定特定疾病在临床上有用的生物标志物。在大多数医学研究上通常使用两个常见的图: 火山图 (volcano plot)和 S图 (S plot)。火山图通常使用 t 检定(或无母数Mann Whitney 检定方法)的p值当作纵轴,两组平均数的比值当作横轴来比较两组代谢物实验数据的结果。S图分别使用每个代谢物特征和其第一主成分的共变异数和相关系数当作横轴和纵轴来作图。但是,过去这两种图只使用在两组代谢物的实验数据上,以找出显着的代谢物特征。本研究放宽火山图两组的条件并合并S图,提出新的视觉化统计图。我们将火山图的横轴改成变异数分析(或非参数 Kruskal Wallis 检定方法)的p值,纵轴改成每个代谢物特征和其第一主成分的相关系数。另外,本研究使用两组实验数据来说明代谢物的鑑定。研究结果显示,本研究提出的方法更容易发现重要的代谢物特征。在火山图和S图两组实验数据中发现显着的代谢物特征,利用本研究提出的方法也都能发现。对于六组实验数据,透过机器学习方法改良的图形复盖率最高。因此,建议当实验是三组以上时,可以利用本研究的视觉化统计方法来发现重要代谢物数据特征。

演讲人4陈蔓桦 (淡江大学统计系)
题目门槛回归模型的竞争风险辅助分配

摘要
门槛回归模型能处理比例风险和非比例风险的问题并做为Cox比例风险模型的一个替代模型。我们将研究重点放在竞争风险资料在门槛回归模型下讨论,并引入维纳过程方法来进一步确认cause-specific hazard、subdistribution hazard、cumulative incidence function (CIF) 之间的关系。我们模拟了 COVID-19 患者临床试验的数据,模拟最近的涉及使用羟氯喹、瑞德西韦和恢复期血浆治疗的试验例子。 我们利用门槛回归模型进一步分析此模拟资料在住院死亡率和康复之间的竞争风险关系。
 
Session 4(多变量分析与机器学习;主持人:薛慧敏教授)
演讲人1林宗仪 (中兴大学应用数学系)
题目Mixtures of Factor Analyzers with Covariates for Clustering Multiply Censored Dependent Variables
摘要
Censored data arise frequently in diverse applications in which observations to be measured may be subject to some upper and lower detection limits due to the restriction of experimental apparatus such that they are not exactly quantifiable. Mixtures of factor analyzers with censored data (MFAC) have been recently proposed for model-based density estimation and clustering of high-dimensional data under the presence of censored observations. We consider an extended version of MFAC with covariates to accommodate multiply censored dependent variables and develop two analytically feasible EM-type algorithms for computing maximum likelihood (ML) estimates of model parameters with closed-form expressions. Moreover, we provide an information-based method to compute asymptotic standard errors of mixing proportions and regression coefficients. The utility and performance of our proposed methodologies are illustrated through several simulated experiments and real data examples.

演讲人2吕恒辉 (东海大学统计系)
题目Interpretable, predictive spatio-temporal models via enhanced Pairwise Directions Estimation
摘要
This article concerns the predictive modeling for spatio-temporal data as well as model interpretation using data information in space and time. Intrinsically, we develop a novel approach based on dimension reduction for such data in order to capture nonlinear mean structures without requiring a prespecified parametric model. In addition to prediction as a common interest, this approach focuses more on the exploration of geometric information in the data. The method of Pairwise Directions Estimation (PDE) is incorporated in our approach to implement the data-driven function searching of spatial structures and temporal patterns, useful in exploring data trends. The benefit of using geometrical information from the method of PDE is highlighted. We further enhance PDE, referring to it as PDE+, by using resolution adaptive fixed rank kriging to estimate the random effects not explained in the mean structures. Our proposal can not only produce more accurate and explainable prediction, but also increase the computation efficiency for model building. Several simulation examples are conducted and comparisons are made with four existing methods. The results demonstrate that the proposed PDE+ method is very useful for exploring and interpreting the patterns of trend for spatio-temporal data. Illustrative applications to two real datasets are also presented.

演讲人3王婉伦 (逢甲大学统计系)
题目A Bayesian Approach to Multivariate Linear Mixed Models with Censored and Missing Responses
摘要
Multivariate longitudinal data usually exhibit complex features such as the presence of censored responses due to detection limits of the assay and unavoidable missing values arising when participants make irregular visits that lead to intermittently recorded characteristics. A generalization of the multivariate linear mixed model constructed by taking into account impacts of censored and intermittent missing responses simultaneously, which is named as the MLMM-CM, has been recently proposed for more precisely analyzing such kinds of data. This paper aims at presenting a fully Bayesian approach to the MLMM-CM for addressing the uncertainties of censored and missing responses as well as unknown parameters. Bayesian computational techniques based on the inverse Bayes formulas (IBF) coupled with the Gibbs scheme are developed for carrying out posterior inference of the model. The proposed methodology is illustrated through a simulation study and a real-data example from the Adult AIDS Clinical Trials Group 388 study. Numerical results show empirically that the proposed Bayesian methodology performs satisfactorily and offers reliable posterior inference.

演讲人4郁方 (政治大学资讯管理系)
题目HiSeqGAN: Hierarchical Sequence Synthesis and Prediction
摘要
High-dimensional data sequences constantly appear in practice. State-of-the-art models such as recurrent neural networks suffer prediction accuracy from complex relations among values of attributes. Clustering data based on their attribute value similarity provides a way to abstract data to clusters in a lower dimension, which could be integrated to improve prediction performance. In this talk, we propose HiSeqGAN, a new generator to synthesize and predict sequences of high dimensional data that are structured in a hierarchy. By relabeling a sample with the cluster that it falls to, we are able to use the GHSOM map to abstract high-dimensional data with clusters in an hierarchical structure that represents its attribute value similarity. We converse the clusters to the coordinate vectors with our hierarchical data encoding algorithm and replace the loss function with maximizing cosine similarity in the SeqGAN model to synthesize cluster sequences. Using the synthesized sequences, we are able to achieve better performance on high-dimension data training and prediction compared to the state-of-the-art models. Specifically, HiSeqGAN adopts a GAN-like learning mechanism. Our generator model is updated by employing a policy gradient and Monte Carlo search, where the final reward signal is provided by the discriminator based on coordinate sequence cosine similarity and is passed back to the intermediate action value. To predict next tokens of training sequences, we use the trained generator to generate sequences that have more periods, and the extended tokens are used as our prediction on sequences that have similar prefix. We have collected two-year real transactions of a semiconductor component distributor to conduct our experiments. The preliminary results show that we can achieve better accuracy compared to standard approaches for sequence prediction on clusters and attribute values, e.g., weekly demands.
 
Session 5(工业统计与应用;主持人:翁久幸教授)
演讲人1杨素芬 (政治大学统计系)
题目A Loss Control Chart for Monitoring Multiple Bottle Cover Weights
摘要
The cosmetic bottle cover weights are collected from a filling machine with multiple identical filling heads every four hours. The cover weights are a critical quality variable of the bottles. The weights variation from each filling head are large, and the weights distributions from each filling head are correlated but not identical. To monitor whether the processes are in-control or out-of-control, the EWMA loss control charts are proposed to demonstrate the in-control samples and detect the out-of-control samples. We find that the proposed EWMA loss control charts perform well.

演讲人2黄怡婷 (台北大学统计系)
题目An Assessment System for Predicting Suitable Range of Heel Height Using Regression
摘要
Wearing high-heeled shoes that exceed the tolerant height might cause severe injuries for females. Such injuries include musculoskeletal pain, osteoarthritis and hallux valgus. The most tolerant height of high-heeled shoes for females can be determined by touching calcaneus to find the deformation of calcaneal varus. Nevertheless, it is not very convenient to visit physician just to find the tolerant height. A novel system was developed to obtain the plantar pressure. 100 females satisfying the inclusion criteria were enrolled in this study. Each participant was asked to stand on the platform imbedding with the 21 sensors to measure the plantar pressure. By adjusting the platform, the plantar pressure for 21 heights was collected. Furthermore, the exact tolerant height of high-heeled shoes of the participant was determined by the physician. Using the regression model, this study included three modules to analyze the data for each feet. We found that the predicted power for the model using the plantar pressure collected from two extreme heights achieves 0.82 and 0.77 for the right and left feet, respectively.

演讲人3蔡志群 (淡江大学数学系)
题目Lamination Design of Photovoltaic Solar Panels
摘要
Solar power is inexhaustible and has become the most appreciative choice in the world. With development stage of solar modules, solar panels are conducted by the relevant reliability tests to ensure long lifetime and power generation efficiency. During the lamination process of solar modules, the performance of the solar panels has been greatly relevant with the degree of crosslinking for EVA sheet. The degree of crosslinking for EVA sheet is obtained by using the extraction method to measure the gel content of EVA sheet. Motivated by lamination tests on solar panels, this study first constructed the statistical model with extreme value residuals to describe the relationship between the degree of cross-linking for EVA sheet and lamination time. Then, under the specification upper and lower limits of the degree of cross-linking for EVA sheet, the optimal lamination time of solar panels will be derived, and the optimal sample allocation for measuring EVA sheets destructively will be addressed.

演讲人4王义富 (中正大学数学系)
题目Degradation Analysis on Trend Gamma Process
摘要
Manufactures always face a big challenge to obtain the sufficient reliability information for high quality products when only a relatively short time period is available for an internal life testing. Fortunately, if quality characteristics (QC) exists, whose degradation over time can be related to reliability, an approach is suggested to collect sufficient degradation data for estimating the product’s lifetime distribution more accurately. In numerical applications, the gamma process (GP) is commonly used when the degradation path is strictly increasing. However, in some circumstances the gamma process is not able to successfully capture the degradation path. In order to tackle this problem, Lawless and Crowder (2004) considered the random effect into the GP model and analyzed the fatigue crack growth data taken from Meeker and Escobar (1998). In this paper, motivated by the effect of trend function proposed by Lindqvist et al. (2003), we propose a trend gamma process (TGP) which is integrated the merits of trend function into a GP model, as an alternative approach to overcome this obstacle. The proposed TGP model is a generalized formulation of GP model, and it is attempted to transform a monotonic stochastic process into a gamma process. The results based on simulation studies and real data examples illustrate our proposed TGP model is suitable for widely use when the degradation path is monotonically increasing.