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2017.7-2018.6
  • Zhang, Y. and Yang, L. ,2018. A smooth simultaneous confidence band for correlation curve. TEST27(2),247-269.
  • Zhang, R., Deng, W., Zhu, Y. ,2017. Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications. Proceedings of the 9th Asian Conference on Machine Learning (ACML17), Seoul, Korea, 2017.
  • Pan, C. and Zhu, M. ,2017. Group Additive Structure Identification for Kernel Nonparametric Regression. Advances in Neural Information Processing Systems 30 (NIPS 2017).
  • Huang, Q. and Zhu, Y. 2017. SPOT: Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regression Analysis. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017).
  • Cheng, L., Zeng, P., and Zhu, Y. (2017) BS-SIM: An Effective Variable Selection Method for High-dimensional Single Index Model. Electronic Journal of Statistics, 11(2) 3522-3548.
  • 邓柯 (2017) 统计学与人文研究的哲学思辨.《公共管理评论》, 2017年第3期(总第26期), 24-38.
  • Sun Z. Wang T., Deng K., Wang X.F., Lafyatis R., Ding Y., Hu M., Chen W. (2018) DIMM-SC: a Dirichlet Mixture Model for Clustering Droplet-Based Single Cell Transcriptomic Data. Bioinformatics34(1), 139-146.
  • Li, D., Zhang, X.F., Zhu, K. and Ling, S. (2018) The ZD-GARCH model: A new way to study heteroscedasticity. Journal of Econometrics 202, 1-17.
  • Liu, F., Li, D.* and Kang, X.M. (2018) Sample path properties of an explosive double autoregressive model. Econometric Reviews 37, 484-490.
  • HouL., Sun, N., Mane, S., Sayward, F., 2017. Impact of Genotyping Errors on Statistical Power of Association Test in Genomic Analyses: A Case Study. Genetic Epidemiology , 41, pp.152-162.
  • Williams, K.R., Colangelo, C.M., Hou, L. and Chung, L., 2017. Use of a Targeted Urine Proteome Assay (TUPA) to identify protein biomarkers of delayed recovery after kidney transplant. Proteomics Clin Appl 11, pp.7-8.
  • Can,A., Castro, V.M., Ozdemir, Y.H., Dagen, D., Dligach, D., Finan, S., Yu,S., Gainer,V., Shadick, N.A., Murphy, S., Cai, T.C., Savova, G., Weiss, S.T., Du, R.*,2018. Alcohol Consumption and Aneurysmal Subarachnoid Hemorrhage. Translational Stroke Research 9(1), pp.13-19.
  • Can,A., Castro, V.M., Yu, S., Dligach, D., Finan, S., Gainer, V., Shadick, N.A., Savova, G., Murphy, S., Cai, T., Weiss, s.t. and Du, R*. 2018. Antihyperglycemic Agents are Inversely Associated with Intracranial Aneurysm Rupture. Stroke 49(1), 34-39.
  • Yu, S., Ma, Y., Gronsbell, J., Cai, T., Ananthakrishnan, A.N., Gainer, V.S., Churchill, S.E., Szolovits, P., Murphy, S.N. and Kohane, I.S., 2017. Enabling phenotypic big data with PheNorm. Journal of the American Medical Informatics Association 25(1), 54-60.
  • Can,A., Castro, V.M., Ozdemir, Y.H., Dagen, S., Yu, S., Dligach, D., Finan, S., Gainer, V., Shadick, N.A. and Murphy, S., 2017, Association of Intracranial Aneurysm Rupture with Smoking Duration, Intensity, and Cessation. Neurology 89(13),1408-1415.
  • McCoy Jr, TH., Yu, S., Hart, K.L., Castro, V.M., Brown, H.E., Rosenquist, J.N., Doyle, A.E., Vuijk, P.J., Cai, T. and Perlis, R.H., 2018. High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records. Biological Psychiatry (2018), 83(12), 997-1004.
  • McCoy Jr, TH., Castro, V.M., Hart, K.L., Pellegrini, A.M., Yu,S., Cai, T. and Perlis, R.H.,2018. Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records. Biological Psychiatry, 83(12), 1005-1011.
  • Liu, H., and Yu,B., 2017. Comments on: High dimensional simultaneous inference with the bootstrap. Test 26(4), 740-750.
  • Lin, Q., Zhao, Z., and Liu, J., 2018. On consistency and sparsity of sliced inverse regression in high dimensions. Annals of Statistics 46(2), 580-610.

 

2016.8-2017.7
  • Shao, Q. and Yang, L. (2017) Oracally efficient estimation and consistent model selection for auto-regressive moving average time series with trend. Journal of the Royal Statistical Society Series B 79(2), 507-524.
  • Zheng, S., Liu, R., Yang, L. and Härdle, W. (2016) Statistical inference for generalized additive models: simultaneous confidence corridors and variable selection. TEST 25(4), 607-626.
  • Wang, J., Wang, S., and Yang, L. (2016) Simultaneous confidence bands for the distribution function of a finite population and its superpopulation. TEST25(4), 692-709.
  • Li, D. and Tong, H. (2016) Nested sub-sample search algorithm for estimation of threshold models. Statistica Sinica 26(4), 1543-1554.
  • Hou L., Sun N., Mane S., et al. (2016) Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study. Genetic Epidemiology 41(2):152-162.
  • Yong F.H., Tian L., Yu S., Cai T. and Wei L.J. (2016) Optimal stratification in outcome prediction using baseline information; Biometrika, 103.4: 817-828.
  • Castro V.M., Dligach D., Finan S., Yu S., Can A., Abd-El-Barr M., Gainer V.S., Shadick N.A., Murphy S.N., Cai T., Savova G., Weiss S.T., Du R. (2017) Large-scale identification of subjects with cerebral aneurysms using natural language processing. Neurology 88(2),164-168.
  • Yu S., Chakrabortty A., Liao K.P., Cai T., Ananthakrishnan A.N., Gainer V.S., Churchill S.E., Szolovits P., Murphy S.N., Kohane I.S., Cai T. (2017) Surrogate-assisted Feature Extraction for High-throughput Phenotyping. Journal of the American Medical Informatics Association 24(el), e143-e149

 

2015.7-2016.7
  • Shao Q. and Yang L. (2016) Oracally effcient estimation and consistent model selection for auto-regressive moving average time series with trend. Journal of the Royal Statistical Society Series B. DOI: 10.1111/rssb.12170.
  • Wang J., Wang S., and Yang L. (2016) Simultaneous confdence bands for the distribution function of a fnite population and its superpopulation. TEST 25(4), 692-709
  • Zheng S., Liu R., Yang L. and Härdle W. (2016) Statistical inference for generalized additive models: simultaneous confdence corridors and variable selection. TEST  25(4), 607-626
  • Yang M., Xue L. and Yang L. (2016) Variable selection for additive model via cumulative ratios of empirical strengths total. Journal of Nonparametric Statistics 28(3), 595-616.
  • Wu H. and Zhu Y. (2016) Deconvolution of base pair level RNA-Seq read counts for quantification of transcript expression levels. Annals of Applied Statistics. (To Appear)
  • 邓柯,陈孟裕,金锋,焦阳,丛林晔,罗季阳,殷杰(2016)中国进口食品风险评估的统计学方法。 《数理统计与管理》 ,已接收。
  • Deng K., Bol P.K., Li K.J., and Liu J.S. (2016) On unsupervised Chinese text mining. Online published in Proceedings of the National Academy of Sciences of USA. DOI: 10.1073/pnas.1516510113.
  • Zang C, Wang T., Deng K., et al (2016) High-dimensional genomic data bias correction and data integration using MANCIE. Online published in Nature Communications. DOI: 10.1038/ncomms11305.
  • Deng K., Li Y., Zhu W., and Liu J.S. (2016) Fast parameter estimation in loss tomography for networks of general topology. Online published in Annals of Applied Statistics. DOI: 10.1214/15-AOAS883
  • Li D., Ling S. and Zakoïan J.M. (2015) Asymptotic inference in multiple-threshold double autoregressive models. Journal of Econometrics 189, 415-427.
  • Li D., Ling S., and Zhang R.M. (2016) On a threshold double autoregressive model. Journal of Business & Economic Statistics 34, 68-80.
  • Li D., and Tong H. (2016) Nested sub-sample search algorithm for estimation of threshold models. Statistica Sinica. 26,4, 1543-1554.
  • Liu F., Li D., and Kang X.M. (2016) Sample path properties of an explosive double autoregressive model.Econometric Reviews. (To Appear)
  • Evans B., Gloria-Soria A., Hou L., McBride C., Bonizzoni M., Zhao H., Powell J. (2015) A multipurpose,high-throughput single-nucleotide polymorphism chip for the Dengue and Yellow Fever Mosquito, Aedes aegyptiG3, 3(5): 711-718.
  • Castro V., Shen Y., Yu S., Finan S., Pau C.T., Gainer V., Keefe C.C., Savova G., Murphy S.N., Cai T., Welt CK.(2015) Identifcation of subjects with polycystic ovary syndrome using electronic health records. Reproductive Biology and Endocrinology 13(1):1.
  • Cai T., Giannopoulos A.A., Yu S., Kelil T., Ripley B, Kumamaru K.K., Rybicki F.J., and Mitsouras D.*. (2016) Natural Language Processing Technologies in Radiology Research and Clinical Applications. RadioGraphics, 36(1): 176-191.
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On July 28, 2018, the 2018 International Statistical Conference (2018 JSM) was held in Vancouver, Canada. The Center for Statistics and Research of Tsinghua University sent a delegation to attend the conference and achieved fruitful results.

JSM is the largest academic activity in the international statistical community. It is an annual event for global statisticians. Thousands of experts, scholars and industry professionals from dozens of countries around the world participated in this event to discuss statistics. The latest scientific research results, breakthroughs and progress in applications, methods, and theories, as well as data science and other related issues. JSM has also established an open and shared platform for statisticians in the statistics and industry, enabling professionals to exchange ideas, establish mutually beneficial cooperation, and jointly promote the development of statistics.

Academic report

During the JSM conference, Professor Yu Sheng introduced Tsinghua Statistics to statistical scholars, experts and aspiring young people from all over the world. The center introduced the statistics discipline of Tsinghua University, which effectively enhanced the international recognition and academic influence of Tsinghua Statistics, and further promoted Tsinghua Statistics to the international platform.

Professor Yu Sheng

Academic Committee

On August 1st, local time, during the JSM annual meeting, the academic committee of the Center for Statistics Science of Tsinghua University met. The Academic Committee held discussions on the development of the central discipline during the JSM meeting each year. Several members were generally concerned about the current echelon construction, the training and promotion of young talents, and the positioning and development of the center. When it comes to the existing framework, we should actively promote open source projects and use the foundations and resources of the government and industry to help the center develop faster.

 

Academic committee discussion

Academic committee member and center representative

Discipline Development and Recruitment Presentation

On July 30th, local time, the Center for Statistics and Research of Tsinghua University successfully held the “Disciplinary Development and Recruitment Seminar” during the JSM meeting. This presentation aims to introduce in detail the development of the Statistics Department of Tsinghua University and the outstanding achievements of the Center for Statistics Research, and encourage high-level overseas statistical talents to join Tsinghua University to jointly promote the development of Tsinghua’s statistical discipline.

At the presentation, Prof. Yu Sheng from the center introduced the research, academic achievements and team composition of the center. Finally, Professor Niu Xiaoyue, the center’s visiting professor and deputy director of the Statistical Counseling Center, explained the positioning of Tsinghua University’s statistical consulting center and the rapid development in a short period of one year and the future development direction. The presentation attracted about 40 people from all walks of life and Tsinghua alumni, and achieved good publicity and expanded the industry’s influence and appeal.

Tsinghua University JSM presentation meeting site

Group photo of the delegation

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From June 8 to 10, 2018, the summer camp for outstanding college students at the Center for  Statistic Science of Tsinghua University was successfully held . The event attracted more than 300 undergraduate students from all colleges and universities across the country to sign up. After several rounds of screening, 36 students from 19 universities including Tsinghua University, Renmin University of China, and Nankai University,University of Science and Technology of China, Shandong Normal University and Northwest Normal University were enrolled in the event.

Academic Report Examination and Presentation
The Award Ceremony
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On June 4th, 2018. Prof. Suojin Wang from Texas A&M University visits the center and gives a talk titled“A simultaneous confidence band for the variance function of functional data”。

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CONFERENCE INFORMATION ORGANIZERS KEYNOTE SPEAKERS PROGRAM REGISTRATION LOCAL INFORMATION CONTACT US
KEY DATES
May 20th, 2018 11:00 AM Registration open
August 1st, 2018 10:00 AM Payment open
September 1st, 2018 05:00 PM Early registration deadline
September 30th, 2018 05:00 PM Registration & Cancellation deadline

Ⅰ. Conference Information

With the rapid development of modern computing technology and the rise of the big data, opportunities and challenges in statistical computing have been witnessed in all dimensions of data science. Confronted to such condition, the Asian Regional Section of the International Association for Statistical Computing (IASC-ARS) and Chinese Association for Statistical Computing (IASC) jointly hold the IASC-ARS 25th Anniversary Conference & CASC 2nd Annual Conference in Beijing, China. The conference is dedicated to promoting further development of statistical computing in modern data science, and to providing a platform for international academic exchanges and cooperation among Statistical Computing and data science professionals.

TimeNovember 9 – 11, 2018

Venue Beijing Conference Center, Beijing,China

(No. 88 Laiguangying West Road, Chaoyang District, Beijing)

Ⅱ. Organizers

The Asian Regional Section of the International Association for Statistical Computing (IASC-ARS)

The Asian Regional Section (ARS) of the International Association for Statistical Computing (IASC) is established by the Council in accordance with art. 12 of the IASC Statutes in 1993 by the name of the East Asian Regional Section (EARS) and the name is changed to the Asian Regional Section in 1998. It is composed of the members of the IASC residing in Asia. Our mission: to organize international or regional seminars, conferences, meetings. In particular, the ARS shall be responsible for arranging Asian meetings on computational statistics, which are abbreviated to IascAsian meetings hereafter; to promote research and training programs on theoretical and practical aspects of computational statistics; to foster evaluations of statistical computing techniques and programs; to publish scientific periodicals and books, reports and newsletters, independently or in cooperation with other organizations; to collaborate with other bodies having similar objectives.

Chinese Association for Statistical Computing (CASC)

The recent advancement of statistical research and increasing requirements arisen from the big data in various application fields have provided unprecedented challenges and opportunities in statistical computing. In order to respond to the pressing needs and seize the historical opportunity, a group of young statisticians, led by Professor Ke Deng of Center for Statistical Science at Tsinghua University, have jointly initiated the Chinese Association for Statistical Computing, as a branch of Chinese Association for Applied Statistics. The CASC was launched on March 19, 2017. The secretariat of CASC is located in the Center for Statistical Science at Tsinghua University.

CASC aims to unite the professionals in both academy and industry to enhance research and applications of statistical computing, towards boosting new theories and methodologies, promoting the applications in health, economics, industry, and other related fields, and advancing the development of statistics in China and overseas.

Co-organizer: Center for Statistical Science of Tsinghua University (TCSS)

On May 16, 2014, the Center for Statistical Sciences of Tsinghua University (TCSS) was officially launched by the university council. The Center will foster and coordinate the development of statistical science in Tsinghua University. The goal is to build a powerful faculty team, to develop cutting-edge research, to strengthen collaborations between disciplines, and, in short, to establish a world-class center of excellence in statistics at Tsinghua University. The Center is an independent academic unit at the school level, while for administrative affairs it is affiliated to the Department of Industrial Engineering.

The Center is dedicated to promoting research and teaching of statistics in Tsinghua. Research areas include mathematical statistics, computational statistics, biostatistics, with emphasis on interdisciplinary studies, especially between statistics and life sciences, medicine, engineering, and business. With their recognized research excellence, engineering, business, and life sciences at Tsinghua University are well placed to benefit from as well as provide nourishment to statistical science. By integrating research resources across campus, The Center has set its goal on high impact academic achievements in both theoretical and applied statistics.

Ⅲ. Keynote Speakers

Coming soon…

Ⅳ. Program

Coming soon…

Ⅴ. Registration

1.   Registration Date

Early Registration: Before September 1, 2018

Regular Registration: September 1-30, 2018

Registration deadline: September 30, 2018

2.   Registration Fee

Early Registration Regular Registration
Regular 960 RMB 1200 RMB
Student 600 RMB 750 RMB

Note: Luncheons and a banquet will be covered by the sponsor during the conference. All participants are responsible to their own accommodation and transportation.

Accommodation Fee

Standard Room at Building 6# Standard Room at Building 9#
Price 550 RMB 650 RMB

Your room contains a piece of breakfast. The accommodation fee will be charged when you check in.

3 Registration Procedure

Step 1: Please fill the registration information.

Step 2: Please click the link to charge the registration fee.

Registration fee will be charged by August.

(1)Online registration

Oops! We could not locate your form.

Please send an email to qianbao@mail.tsinghua.edu.cn before Oct. 1st if your schedule is changed.

Oops! We could not locate your form.

 (2)Payment:

E-currency payment: Master, Visa

Ⅵ. Contact Us

Secretariart for Chinese Association for Statistical Computing
Address: Weiqing Building 212, Tsinghua University, Beijing 100084, China

Tel: +86 (10)-62786091

Fax:+86 (10) -62783842

Email:stats@tsinghua.edu.cn

ⅥⅠ. Local Information

Venue: The Beijing Conference Center

Address: No. 88 Laiguangying West Road, Chaoyang District, Beijing

Tel: 86(10)-84901668

http://www.beijinghuiyizhongxin.com/encontact/176.html

http://www.google.cn/maps/place/Beijing+Conference+Center/@40.0105258,116.4077197,13.75z/data=!4m5!3m4!1s0x35f1aab720d5746b:0xad5ff040d3d33ade!8m2!3d40.021009!4d116.42843?hl=en

Local Transportation

Local transportation from the airport to the conference center

 

Hotel Information

Address: No. 88 Laiguangying West Road, Chaoyang District, Beijing

Tel: 86(10)-84901668

http://www.beijinghuiyizhongxin.com/encontact/176.html

Standard Room

Scenic Spots in Beijing

Tian’anmen (the Gate of Heavenly Peace)

Tian’anmen (the Gate of Heavenly Peace), is located in the center of Beijing. It was first built in 1417 and named Chengtianmen (the Gate of Heavenly Succession). At the end of the Ming Dynasty, it was seriously damaged by war. When it was rebuilt under the Qing in 1651, it was renamed Tian’anmen, and served as the main entrance to the Imperial City, the administrative and residential quarters for court officials and retainers. The southern sections of the Imperial City wall still stand on both sides of the Gate. The tower at the top of the gate is nine-room wide and five –room deep. According to the Book of Changes, the two numbers nine and five, when combined, symbolize the supreme status of a sovereign. During the Ming and Qing dynasties, Tian’anmen was the place where state ceremonies took place. On October 1, 1949, chairman Mao Zedong proclaimed on Tian’anmen Rostrum the founding of the People’s Republic of China. Since then Tian’anmen has been the symbol of New China.

 

The Great Wall of China

The Great Wall of China is a series of fortifications made of stone, brick, tamped earth, wood, and other materials, generally built along an east-to-west line across the historical northern borders of China to protect the Chinese states and empires against the raids and invasions of the various nomadic groups of the Eurasian Steppe. Several walls were being built as early as the 7th century BC; these, later joined together and made bigger and stronger, are collectively referred to as the Great Wall. Especially famous is the wall built in 220–206 BC by Qin Shi Huang, the first Emperor of China. Little of that wall remains. The Great Wall has been rebuilt, maintained, and enhanced over various dynasties; the majority of the existing wall is from the Ming Dynasty (1368–1644).

Apart from defense, other purposes of the Great Wall have included border controls, allowing the imposition of duties on goods transported along the Silk Road, regulation or encouragement of trade and the control of immigration and emigration.

The Great Wall stretches from Dandong in the east to Lop Lake in the west, along an arc that roughly delineates the southern edge of Inner Mongolia. A comprehensive archaeological survey, using advanced technologies, has concluded that the Ming walls measure 8,850 km (5,500 mi). This is made up of 6,259 km (3,889 mi) sections of actual wall, 359 km (223 mi) of trenches and 2,232 km (1,387 mi) of natural defensive barriers such as hills and rivers. Another archaeological survey found that the entire wall with all of its branches measures out to be 21,196 km (13,171 mi). Today, the Great Wall is generally recognized as one of the most impressive architectural feats in history.

The Temple of Heaven

The Temple of Heaven (Chinese: 天坛; pinyin: Tiantan) is an imperial complex of religious buildings situated in the southeastern part of central Beijing. The complex was visited by the Emperors of the Ming and Qing dynasties for annual ceremonies of prayer to Heaven for good harvest. It has been regarded as a Taoist temple, although Chinese heaven worship, especially by the reigning monarch of the day, predates Taoism.

In ancient China, the Emperor of China was regarded as the Son of Heaven, who administered earthly matters on behalf of, and representing, heavenly authority. To be seen to be showing respect to the source of his authority, in the form of sacrifices to heaven, was extremely important. The temple was built for these ceremonies, mostly comprising prayers for good harvests.

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On May 25th, 2018, Prof. Ke Deng  was Invited by The 11th China-R Conference and gave a talk titled “Statistics and the Health Industry in China.”

Prof. Deng

 

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