Selected Reports

Selected Reports [When something can be read without effort, great effort has gone into its writing.--- Lectures para analfabetos; 讀來全不費工夫的文章，寫的時候一定要下過大工夫。]

“Any fool can make things bigger, more complex and more violent. It takes a touch of genius – and a lot of courage – to move in the opposite direction.” – Albert Einstein, Physicist

Refereed papers, Technical reports & Manuscripts, Conference papers, Referred Conference Papers, Articles in books, Invited talks, Miscellaneous

Refereed Papers:

Zhuojian Chen, Zhanfeng Wang*, and Yuan-chin Ivan Chang* (2023). Distributed sequential estimation procedures, The Canadian Journal of Statistics. https://doi.org/10.1002/cjs.11762
Yogesh M. Tripathi, Suneel Babu Chatla, Yuan-chin Ivan Chang, Li-Shan Huang and Grace S. Shieh* (2022). A Nonlinear Correlation Measure with Applications to Gene Expression Data, Plos One (accepted).
Zhanfeng Wang, Amy M. Kwon, Yuan-chin Ivan Chang* (2022). Active learning with logistic models featuring simultaneous variable and subject selection, (Communications in Statistics - Simulation and Computation, February 14, 2022). https://doi.org/10.1080/03610918.2022.2037636
Chia-Jung Li, Yi-Han Chiu, Chung Chang, Yuan-chin Ivan Chang, Jim Jinn-Chyuan Sheu, An-Jen Chiang* (2021). Acetyl coenzyme A synthase 2 acts as a prognostic biomarker associated with immune infiltration in cervical squamous cell carcinoma , Cancers 2021, 13(13), 3125;https://www.mdpi.com/2072-6694/13/13/3125/htm
Bo-Shiang Ke and Yuan-chin Ivan Chang* (2021). A model-free subject selection method for active learning classification procedures, Journal of Classification (Accepted, 2021/03/24)
An-Jen Chiang, Chia-Jung Li, Chung Chang, Yuan-chin Ivan Chang, Li-Wen Chen, Tsung-Hsien Chang*, Jim Jinn-Chyuan Sheu* (2020). UBE2C Drives Human Cervical Cancer Progression and Is Positively Modulated by mTOR, Molecular Pathology, New Advances in Molecular Oncology
Jingjing Li, Zimu Chen, Zhafeng Wang and Yuan-chin Ivan Chang* (2020). Active learning in multiple-class classification problems, Computational Statistics and Data Analysis (Accepted, 2020-01-01). https://doi.org/10.1016/j.csda.2020.106911
Zimu Chen, Zhanfeng Wang and Yuan-chin Ivan Chang* (2020). Sequential adaptive variables and subject selection for GEE methods, Biometrics, 76, no. 2, 496 -- 507. https://doi.org/10.1111/biom.13160
Yuan-chin Ivan Chang* and Ray-Bing Chen (2019). Active learning with simultaneous subject and variable selections, Neurocomputing, 329, 495 -- 505. https://doi.org/10.1016/j.neucom.2018.11.036
Hsiang-Ling Hsu, Yuan-chin Ivan Chang and Ray-Bing Chen* (2019) Greedy Active Learning Algorithm for Logistic Regression Models, Computational Statistics and Data Analysis, 129, 119 -- 134. https://doi.org/10.1016/j.csda.2018.08.013
Wong-Shian Huang and Yuan-chin Ivan Chang* (2018). Sample size determination and treatment screening in two-stage Phase II clinical trials via ROC curve, Pharmaceutical Statistics, 17, 504 -- 514. DOI:10.1002/pst.1866.
Wenbao Yu, Yuan-chin Ivan Chang, Eunsik Park*(2018). Applying a modified AUC to gene ranking, Communications for statistical applicastions and methods, 25 (3), 307 -- 319. dx.doi.org/10.29220/CSAM.2018.25.3.307
Bo-Shiang Ke, An Jen Chiang and Yuan-chin Ivan Chang* (2017). Influence Analysis for the Area Under the Receiver Operating Characteristic Curve, Journal of Biopharmaceutical Statistics, 28, no. 4, 722 - 734. (10.1080/10543406.2017.1377728)
Yuan-chin Ivan Chang (2016). Multiple-class classification: ordinal and categorical labels, Communications in Statistics - Simulation and Computation, 46, no. 10, 7561--7581. doi.org/10.1080/03610918.2016.1242732
Eunsik Park and Y-c. I. Chang (2016). Multiple-stage sampling procedure for covariate-adjusted response-adaptive designs, Statistical Methods in Medical Research, 25, No. 4, 1490 -- 1511. (SCI)
B. Jia, Z. Wang and Y-c. Ivan Chang (2015). Assessing the diagnostic power of variables measured with a detection limit, Computational Statistics (Accepted 2015-10-18).
Shen, SH, Shen, SY, Liou, TH, Hsu, MI, Chang, Yuan-chin I, Cheng, CY, Hsu, CS, Tseng, CR (2015). Obesity and inflammatory biomarkers in women with polycystic ovary syndrome, European Journal of Obstetrics & Gynecology and Reproductive Biology, 192, 66–71.(SCI)
Z. Wang, X. Luo, and Yuan-chin Ivan Chang*(accepted 2015). Assessing the predictive power of newly added biomarkers, Biometrical Journal (accepted, February, 2015).(SCI)
W. Yu, E. Park, Y-c. Ivan Chang (2015). Comparison of paired ROC curves through a two-stage test, Journal of Biopharmaceutical Statistics, 1-22.(SCI)
Chii-Ruey Tzeng+, Yuan-chin Ivan Chang+, Yu-chia Chang, Chia-Woei Wang, Chi-Huang Chen, Ming-I Hsu* (2014).Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age, Fertility and Sterility ,Volume 101, Issue 5, 1404–1410. +The first two authors were similar in author order, * corresponding author.
Man-Jen Hsu*, Y-c. Ivan Chang and Huey-Miin Hsueh (in press). Biomarker Selection for Medical Diagnosis using the Partial Area under the ROC Curve, BMC Research Notes.(SCI)
W. Yu, Yuan-chin Ivan Chang and E. Park* (2014). A modified area under the ROC curve and its application to marker selection and classification, Journal of Korean Statistical Society, Volume 43, Issue 2, 161–175
Zhanfeng Wang and Y-c. I. Chang* (2014). A general AUC-type measure and biomarkers selection, Statistics in Biopharmaceutical Research, 6:1, 89-103, (DOI:10.1080/19466315.2013.856811)(SCI)
Sung-Chiang Lin, Charlotte Wang and Yuan-chin Ivan Chang (2014). Challenges of Statistical and Machine Learning on Supervised Learning with Class-Imbalanced Data, Journal of Chineses Statistical Association, 52, No.1, 59 -- 84.
Yi-Hui Lin, Shih-Yi Huang, Ming-I Hsu*, Yuan-chin Ivan Chang, Chih-Yu Cheng, Chun-Sen Hsu, Chii-Ruey Tzeng (2013). Hyperhomocysteinaemia is associated with biochemical hyperandrogenaemia in women of reproductive age, European Journal of Obstetrics & Gynecology and Reproductive Biology, 171, 2, 314 -- 318.(SCI)
Pao-Hwa Lin*, Wen-Ting Yeh, Laura P Svetkey, Shao-Yuan Chuang, Yuan-chin Ivan Chang, Christine Wang, Wen-Harn Pan (2013). Dietary intakes consistent with the DASH dietary pattern reduce blood pressure increase with age and risk for stroke in a Chinese population, ASIA PACIFIC JOURNAL OF CLINICAL NUTRITION, 22(3):482-91.
Zhanfeng Wang and Y-c I. Chang* (2013). Sequential estimate for linear regression models with uncertain number of effective variables, Metrika 76, 7, 949--978 (doi:10.1007/s00184-012-04260-4). (SCI)
Y-c. I. Chang (2013). Maximizing an ROC-type measure via linear combination of markers when the gold reference is continuous, Statistics in Medicine, Volume 32, No. 11: 1893 -- 1903. (SCI)
Y-c. I. Chang and E. Park (2013). Sequential estimation for covariate-adjusted response-adaptive designs, Journal of the Korean Statistical Society Volume 42, Issue 1,: 105 -- 116. (SCI)
Zhanfeng Wang, Chen-An Tsai*, Y-c. I. Chang* (2012). Identifying Differential Gene Sets using the Linear Combination of Genes with Maximum AUC, Journal of Proteomics & Bioinformatics, Volume 5(3): 073-083.
Shih-Chia Chen, Charlotte Wang and Yuan-chin Ivan Chang* (2011). Sequential estimate of partial area under ROC curve with $\beta $-protection and optimal ratio of cases to controls, Journal of Statistical Planning and Inference 141, 3356–3366.
Y-c. I. Chang (2011). Sequential estimation in generalized linear models when covariates are subject to errors, 73, 93 -- 120, Metrika.(SCI)
C-Y. Chien, Y-c. I. Chang and H-M. Hsueh*(2011). Optimal sampling in retrospective logistic regression via two-stage method, Biometrical Journal, 53, No. 1, 5 -- 18 .
Zhanfeng Wang and Y-c. I. Chang* (2011). Markers selection via maximizing the partial area under ROC curve of linear risk scores, Biostatistics, 12(2): 386 - 398.
Y-c. I. Chang, Yufen Huang* and Y. Huang (2010). Early stopping of L2Boosting, Computational Statistics and Data Analysis, 54, 2203 -- 2213.
Y-c. I. Chang* and H-Y Lu (2010). Online Calibration Via Variable Length Computerized Adaptive Testing, Psychometrika, 140 -- 157 .
Eunsik Park and Y-c. I. Chang* (2010). Sequential analysis of longitudinal data in a prospective nested case-control study, Biometrics, 1034 -- 1042, and its web supplementary.(
Sung-Chiang Lin, Yuan-chin Ivan Chang, W.-N. Yang (2009). Meta-learning for Imbalanced Data and Classification Ensemble in Binary Classification, Neurocomputing, 73, 484–494.
Y-c. I. Chang and Eunsik Park* (2009). Constructing the best linear combination of diagnostic markers via sequential sampling, Statistics and Probability Letters, 79, No. 18 1921-192 .
Zhanfeng Wang, Yuan-chin Ivan Chang, Zhiliang Ying, Liang Zhu, Yaning Yang* (2007). A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve, Bioinformatics 2007 23(20):2788-2794(SCI) . (Software) (Software: R-package for MS Windows -- Note that to use this package, IMSL library is required. IMSL independent version is underdevelopment.)
Andy C. Tsao* and Yuan-chin Ivan Chang (2007). A Stochastic Approximation View of Boosting, Computational Statistics & Data Analysis, 52, 1, 325 - 334. (SCI)
Yuan-chin Ivan Chang (2005). Application of Sequential Interval Estimation to adaptive mastery testing, Psychometrika, Volume 70, 4, 685 - 713 .(SCI, SSCI)
John F. Young, Weida Tong, Hong Fang, Qian Xie, Bruce Pearce, Ray Hashemi, Richard D. Beger, Mitchell A. Cheeseman, James J. Chen, Yuan-chin Ivan Chang, and Ralph L. Kodell (2004). Building an Organ-Specific Carcinogenetic Database for SAR Analysis, J. Toxicol Environ Health, Part A, 67, pp1363-1389. (SCI)
Yuan-chin Ivan Chang* and Z. Ying (2004). Sequential estimation in variable length computerized adaptive testing, Journal of Statistical Planning and Inference, Vol. 121, Issue 2, pp 249-264. (SCI)
Yuan-chin Ivan Chang (2004). Application of Sequential Probability Ratio Test to Computerized Criterion-Referenced Testing, Sequential Analysis, Vol. 23, No. 1, pp 45-61.
Lin, M.H., Huang, S.Y. and Y-c. Ivan Chang (2004). Kernel-based discriminate techniques for educational placement. Journal of Educational and Behavioral Statistics, 29, pp 219-241.(SSCI)

(For related software and data sets used in above papers, please click here.)

Technical Reports and Manuscripts:

Wan-Ping Nicole Chen, Yuan-chin Ivan Chang (2022). Determination of class-specific variables in nonparametric multiple-class classification, arXiv:2205.03623 http://arxiv.org/abs/2205.03623
Zhanfeng Wang, Yumi Kwon, Yuan-chin Ivan Chang (2019). Active learning for binary classification with variable selection, arXiv:1901.10079.
Wan-Ping N. Chen and Yuan-chin Ivan Chang (2019). Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation, arXiv:1901.01000.
Zhanfeng Wang and Yuan-chin Ivan Chang (2018). Distributed sequential method for analyzing massive data, http://arxiv.org/abs/1812.09424.
Yuan-chin Ivan Chang (2017). Application of ROC curve to Sample size determination and treatment screening in two-stage Phase-II clinical trials (with W-S Huang, Accepted)
Sequential estimation with falliable Responses (in preparation)
Yuan-chin Ivan Chang (2016). Model-Free Influential Indexes for General Classification Rules with B-S Ke; Accepted for publication in Journal of Classification)
Yuan-chin Ivan Chang (2016). Sequential estimaation under GEE with application to Testlet-based educationa l tests (in preparation, with Z. Chen and Z. Wang)
Yuan-chin Ivan Chang (2016). Active learning via a modified logistic regression model with variable selection (in preparation, with Z. Wang)
Yuan-chin Ivan Chang (2016). Simultaneously seleting subjects and variables in active learning processes via linear models (submitted, with R-B Chen)
Yuan-chin Ivan Chang (2014). Active Learning Via Sequential Design and Uncertainty Sampling, arXiv:1406.4676 (with Jing Wang, Eunsik Park)
Yuan-chin Ivan Chang (2012). An alternative model for testlet-based testing and adaptive testlet-based testing, Manuscript.
Yuan-chin Ivan Chang (2011). Application of sequential methods to testlet based psychological/educational testing, Manuscript.
Y-c. I. Chang* (2009). On-line Calibration Problems in Computerized Adaptive Testing.

Refereed Conference Papers:

Yuan-chin Ivan Chang and Sung-Chiang Lin (2004). Synergy of Logistic Regression and Support Vector Machine In Multi-class Classification, Z. R. Yangm, R. Everson and H. Yin Eds.:Intelligent Data Engineering and Automated Learning-IDEAL 2004,LNCS 3177(ISSN 0302-9743), pp 132 -- 141, Springer, Berlin Heidelberg.(SCI, EI)
Yuan-chin Ivan Chang, Horan Hsu and Lin-Yi Chou (2002). Graphical Features Selection Method, H. Yin et al. (Eds.): Intelligent Data Engineering and Automated Learning-IDEAL 2002, LNCS 2412, pp. 475–480, Springer, Berlin Heiferlberg New York.(SCI, EI)

Articles in Books:

Yuan-chin Ivan Chang, Yuh-Jye Lee, Hsing-Kuo Pao, Mei-Hsien Lee, and Su-Yun Huang (2008). Data Visualization via Kernel Machines, Handbook of Computational Statistics (Volume III)- Data Visualization Ed. by Chun-houh Chen, Wolfgang Hardle and Antony Unwin. ( also to be presented in workshop on Data and Information Visualization 2006, Berlin).
Yuan-chin Ivan Chang and A. Martinsek (2004). Sequential Approaches to Data Mining, In Applied Sequential Methodologies, ed. N. Mukhopadhyay, S. Datta, S. Chattopadhyay, 85-103. Marcel Dekker, Inc.: New York.

Conference Papers:

Statistical Methods and Active Learning -- simultaneous subject and variable selection, 8th International Conference on Risk Analysis and Design of Experiments, Vienna, April 23 – 26 , 2019.
Sequential Adaptive Subject And Variable Selection For Generalized Estimating Equation Methods, VIth ISBS Kyoto, 2019
Adaptive Testing, sequential experimental design and active Learning, DSSV 2019, Kyoto, Japan
Active learning classification with variable selection, COMPSTAT 2018, 23rd International Conference on Computational Statistics, Iasi, Romenia
Learning Statistics From Data, ICOTS 10, Kyoto, Japan, 2018
Adaptive Testing, sequential experimental design and active Learning, Statistical Methods for Educational Testing and Learning, Shanghai, 2018
Learning and Using Statistics Liberally, Conference on Education of Data Science, November 1(Wed), 2(Thu), 2017, Hikone Campus, Shiga University
Constructing binary classifiers for drug consumption data via adaptive sampling method, 2017 at ISM, Tachikawa, Tokyo
Application of ROC curve to two-stage Phase II clinical Trial design, Joint Conference on Biometrics & Biopharmaceutical Statistics Vienna (Austria), August 28 - September 1, 2017 - Medical University of Vienna, Spitalgasse 23, 1090 Vienna
Application of the influential index to active learning procedures, IFCS-2017 at Tokyo
Active learning via a linear model, ISI, 2016, Toronto, Canada, 2016
Active learning with general additive models, EMS, Netherland, 2015
Assessinh the prediction power of newly added biomarkers, Workshop on Statistical Methodology and Applications to Biomedicine and Finance, 2015
Additive predictive ability of a new maker within a prefixed range of specificity,IBC 2014, July, 2014
Multiple-class classification, COMPSTAT 2014, Geneva, 2014
Boosting diagnosis performance with nonparametric logistic type classification function, ISCB 35th,Vienna, Austria, August, 2014
Performance and Interpretation Ability of Classification Models, Chih-Li Sung and Yuan-chin Ivan Chang, 2014.
A general AUC-type measure and its corresponding optimal linear combination of variables, The 15th ASMDA2013 International Conference, June 23 ~ 28, 2013.
Finding the Optimal Linear Combination of Markers that Maximizes an AUC-type Measure When the Gold Standard is Continuous (based on Obuchowski's index), 26th IBC Kobe, Japan. August 26-31, 2012.
Sequential Methods in Testlet-based Testing, 58th Congress (ISI 2011), Dublin, Ireland, August 21-26, 2011.
Area under ROC curve type measures without binary gold standard, International Conference on Applied Statistics and Financial Mathematics (ASFM 2010), The Hong Kong Polytechnic University, December 16 - 18, 2010.
Eunsik Park* and Yuan-Chin I. Chang (2010). Classification Ensemble That Maximizes the Area Under Receiver Operating Characteristic Curve, 19 th International Conference on Computational Statistics, Paris - France, August 22-27, 2010.
Finding best linear combination of markers for a medical diagnostic with restricted false positive rate, The International Biometric Society Australasian Region (IBS AR), Biometrics on the lake, Nov. 28 ~ Dec. 3, 2009, Taupo, New Zealand.
Zhan-Feng Wang and Yuan-chin I. Chang (2009). Markers Selection Method Via A Linear Combination That Maximizes The Partial Area Under The ROC Curve, 30th Annuals Conference of the International Society for the Clinical Biostatistics (Prague, Czech Republic).
Eunsik Park* and Yuan-chin Chang* (2009). Comparison of sequential analysis in response-adaptive designs with and without covariate adjustment, 30th Annuals Conference of the International Society for the Clinical Biostatistics (Prague, Czech Republic).
Eunsik Park, Yuan-chin Chang and Meehye Cho* (2009). Sequential Estimation In Covariate-Adjusted Response-Adaptive Designs Via Multiple Stage Method, 30th Annuals Conference of the International Society for the Clinical Biostatistics (Prague, Czech Republic).
Huey-Miin Hsueh*, Chih-yi Chien, Yuan-chin Ivan Chang (2008). Group Sequential Tests in Generalized Linear Models with MIS-Measured Covariate's, IASC 2008, Yokohama, Japan
Chih-yi Chien*, Yuan-chin Ivan Chang, Hueymiin Hsueh (2008). Case-Control Studies Using Logistic Regression Models When Covariate's are Measured with Errors, IASC 2008, Yokohama, Japan
Charlotte Wang* and Y-c. I. Chang (2008). Sequentially Determine the Optimal Case-Control Ratio for Estimating the Classification Accuracy of a Biomarker, 2008 ISCB 29th, Copenhagen, Denmark, 2008-08-17~21.
Eunsik Park* and Yuan-chin Ivan Chang (2007). Sequential analysis of case-control longitudinal data, 2007 ISI, Lisbon, Portugal, 2007-08-22~29.
Sequential Estimation of Generalized Measurement-error Linear Models with Application to Online Calibration problems of Computerized Adaptive Testing, 2007 ISI, Lisbon, Portugal, 2007-08-22~29.
Yuan-chin Ivan Chang*, Yuh-Jye Lee, Hsing-Kuo Pao, Mei-Hsien Lee, and Su Yun Huang(2006). Data Visualization via Kernel Machines, workshop on Data and Information Visualization 2006, Berlin.
Estimate of ROC curve via kernel machine, (Compstat 2006) , August 28-September 1, 2006, Rome, Italy
(Presented by Dr. Grace Huang) Integrating Discrete Wavelet Transform and Neural Networks for Prostate Cancer Detection Using Proteomic Data, BIOINFO 2005–September 22~24 / BEXCO, Busan, Republic of Korea
(with Ching-Wei Chang) Kernel ROC Curve Analysis, 94 年統計學術研討會 (2005)‧(地點：淡江大學)
Boosting Algorithm that maximizes the area under ROC curve, presented in Computational Statistics and Data Analysis Conference 2005, Cyprus.
C. Andy Tsao* and Yuan-chin Ivan Chang (2005). A Stochastic Approximation View of Boosting, presented in Computational Statistics and Data Analysis Conference 2005, Cyprus.
Sequential methods in some machine learning problems, Workshop on Sequential Analysis, Time Series and Related Topics, Academia Sinica, Taipei , Taiwan, Dec. 27-28, 2004.
Yuan-chin Ivan Chang and Sung-Chiang Lin*. Synergy of Logistic Regression and Support Vector Machine In Multi-class Classification, IDEAL 2004, Exeter, UK(refereed conference).
Yuan-chin Ivan Chang* and A. Martinsek. Sequential Approaches to Data Mining (2002). International Conference on Ranking and selection, multiple comparison, reliability, and their applications, Chennai , India.
with 陳炳霖 and 余清祥‧ 普適提演算法的比較(Comparison of Boosting Algorithm with Different Weaker Learners, (2004, 南區統計研討會暨中華機率統計學會學術研討會及年會)
with 盧宏益‧ Comparison of SPRT and MSPRT on Computerized Mastery Testing, (2004, 南區統計研討會暨中華機率統計學會學術研討會及年會)
從網路教育談起 ( 資料採礦與創新線索研討會-- 政治大學商學院資料採礦研究中心 , 政治大學商學院主辦 )

Invited Talks:

To be (a statistician), or not to be, that is a question, Seikei University, Tokyo, Japan, 2019
Active learning in binary classification problems via stochastic linear models, 2018 JSS meeting at Kanazawa
Project-oriented Teaching in Modern Data Science Er, Conference on Education of Data Science2018, Shiga University
Learning and Using Statistics Liberally, Conference on Education of Data Science, November 1(Wed), 2(Thu), 2017, Hikone Campus, Shiga University
I think therefore I am, 台北榮民總醫院, 2016
Active learning via a linear model, 交通大學, 2016
Assessinh the prediction power of newly added biomarkers, 政治大學, 2016
To be or not to be a Statistician, 中山大學, 2015
Assess of Newly Added Markers, Osaka University, 2015
Something old is new again: Regression and Active Learning, 政治大學 and 中央大學 and 淡江大學, 2015
I think therefore I am, 臺北市立大學數學系, 2015
Toward Big Data Analysis: Classification and Performance Measures, 中山大學, 2015
Discussion on COORDINATE DESCENT ALGORITHMS FOR LASSO PENALIZED REGRESSION, 2015.
BIG DATA -- 新瓶舊酒呢？還是舊瓶新酒？公訓中心, October, 2014
Multiple-class classification, 中山大學, October, 2014
Multiple-class calssification, 中央大學, 2014.
Something old is new again, Department of Mathematics, NTU, 2014.
Something Old Is New Again, Statistics in Modern Big Data Era, Southesat Asia International Joint Research and Training Program (SEAIP) 2013, Taichung, Dec. 2~4, 2013.
Go beyond the Buzzword - Statistics in Big Data Era (2013-08) 東華大學 (2013-12-06)
To be a statistician, or not to be, that is a question, 成功大學 (2013-05), 統計科學營 (2013-09-04), 靜宜大學 (2013-12-05)
Sequential Estimation in Covariate Adjusted Response Adaptive Designs, 臺北大學(2013-04).
Statistics in the Big Data Era - Going beyond the buzzword, 2013 Big Data Forum, Jointly organized by CITI & IIS, Academia Sinica.
Sequential estimation in covariate adjusted response adaptive designs, NHRI (國家衛生研究院) Nov., 2012, 東海大學, Dec., 2012
Application of Sequential Methods with Adaptive Designs, IASC-ARS Special Session and JSCS annual Conference, Tokyo University, Nov. 1~2, 2012.
Measuring the diagnostic power of variables when the gold standard is continuous, 臺北大學, April, 2012.
Item calibration using sequential method and a two-stage adaptive design method, 成功大學, March, 2012.
Finding the optimal linear combination of markers that maximizes the AUC-type measure when the gold reference is continuous (Geometric Based Extension), May 10, 2011 Department of Mathematics, 淡江大學, September 23, 2011, 統計所，交通大學; February 2012, ISM, Japan。
Area under ROC curve type measures without binary gold standard, September 29, 2010, Department of Mathematics, National Chung-Cheng University,
Sequential estimate for linear regression models with uncertain number of effective variables, April 27, 2010, Graduate Institute of Statistics, National Center University.
Markers selection via ROC curve related measures, Department of Mathematics, National Taiwan University, Dec. 25, 2009.
Regression vs. Classification, Linear vs Nonlinear, and Machine Learning, Lectures on Central Weather Bureau, Dec. 21 ~ 23, 2009.
Makers selection via partial Area Under ROC curve, Department of Applied Mathematics, National Dong-Hwa University,Dec. 18, 2009 .
Selecting markers Via ROC curve and its related measures, 模型選取及其相關主題研討會 (model selection and its related topics), Academia Sinica (2009-05-25) and IMS 1st Asia-Pacific Rim Meeting June 28 - July 2, 2009.
Comparison of Diagnostic Powers of Biomarkers Via Sequential Optimal Estimates of Partial Areas Under ROC curve 高雄大學
Comparison of Diagnostic Powers of Biomarkers Via Sequential Optimal Estimates of Partial Areas Under ROC curve, Institute of Statistical Mathematics, Tokyo, Japan (2008-10-02) and The center for advanced Medical Engineering and Informatics, Medical Statistics, Osaka University, Suita, Osaka, Japan-[大阪大學大學院醫學系研究科‧醫學統計學] (2008-10-03), National Kaoshiung University(2009-02-25).
Sequential Methods for analyzing case-control longitudinal data, 中山大學應用數學系 (National Sun Yat-sen University, Department of Applied Mathematics). 2007-04-19
(1) A Brief Introduction To Sequential Methods and Its Applications, and (2) Some Estimation Problems in Generalized Linear Models With Measurement Errors in Covariates. Presented in 2nd Phase of Brain Korea 21, Scientific Computing and Biostatistics, International Symposium on Biostatistics, Nov. 23-Nov 24, 2006, Chonnam National University, Gwang-ju, Korea.
Data Visualization via Kernel Machines, Technical report C-2006-04, Institute of Statistical Science, Academia Sinica, ( Compstat 2006 Satellite Workshop on Data and Information Visualization 2006), August 24-25, 2006, Berlin, German
Boosting Algorithms that maximize the area under ROC curve (2006 海峽兩岸統計研討會, 國家衛生研究院,竹南)
Some Estimation Problems in Generalized Linear Model with Measurement Errors in Covariates (2006), 第十五屆南區統計研討會, 國立中正大學數學系暨統計科學研究所, 2006年 6月 24日 ~ 2006年 6月 25日.
Some Estimation Problems in Generalized Linear Models with Measurement Errors in Covariates (Presented in Taiwan Southern Area Conference, June, 2006; Institute of Statistical Science, Academia Sinica, July, 2006)
Boosting algorithm that maximizes the area under ROC curve (2005, Dec. 7, presented in National University of Kaoshiung)
Many Faces of Boosting Algorithms ( June 2005, National Taipei University )
Stop Boosting! If you know when ( presented in 2004 南區統計研討會暨中華機率統計學會學術研討會及年會)

Miscellaneous: (Most of them are in Chinese)

To be (a Statistician), or not to be, that is a question, 東華大學，Dec. 2012；成功大學, May, 2013; Statistical Science Camp at Academia Sinica, September, 2013.
統計 : 一部用數學語言寫的文學作品 (統計教育的實踐與研究工作坊2011-09- 08, 國立高雄大學統計學研究所 )
知識天地(中央研究院週報, 95年10月12日出版): 是「舊瓶裝新酒」？還是「新瓶裝舊酒」？
音樂與數學 (2007, Feb, 文山國中 "音韻新潭" 音樂冬令營)
如何「讀」統計 (presented in Statistical Science Camp 2005, ISAS)
眾裡尋「它」千百度 -- Adaptive Searching Engine (A short version was presented in Open House Briefing of Inst. of Statistical Science, Academia Sinica, 2004)
如何在網路上找到你想要的文件? (How to find the documents you want on internet?) (presented in Statistics Camp 2004, ISAS)
Mathematics and Music (for Math. Olympic Team, in NTU, 2003)
孔子談網路教學(e-Learning and Confucius, Statistics Education on Web, presented in NCCU, 2003)