In today’s complex business environment, applying quantitative techniques to solve business problems is a key skill. Knowledge of statistical theory and practice is increasingly important for students in many disciplines, such as business, analytics, actuarial sciences, data science, the life sciences, physical sciences, social sciences, and engineering. No matter which profession you choose, studying statistics at the Fox School will give you the training you need to succeed.
The Department of Statistical Science offers several degree options for undergraduates and graduates. Students gain research and work experience through numerous internship opportunities. The faculty brings real-world experience to the classroom, and engages in a variety of scholarly activities, including publication of journal articles and books, attendance at professional conferences, and consulting.
Disciplines & Programs
Choose the Area of Study That is Right for You
A Statistics degree offered by the Department of Statistical Science at the Fox School gives you a competitive advantage in a world where data is everything. Whether planning to enter the business world as a data scientist or the founder of the next hot startup, or to work in government, research institutes, pharmaceutical, health, engineering or social sciences, the Department provides the training needed to help you achieve your career goals.
Advance Business Through Research
The renowned faculty at the Department of Statistical Science are research leaders in a variety of traditionally important and emerging areas of the discipline including:
- High-Dimensional Statistics
- Biostatistics and Bioinformatics
- Decision Neuroscience
- Theory Based Methodological Research
- Bayesian Methods
- Big Data
Centers and Institutes
The Biostatistics Research Center, the Center for Statistical Analysis, and the Center for High-Dimensional Statistics are department hubs that promote the research of faculty and students. They also offer statistical consulting to the Temple University community and beyond, and explore several emerging fields in the discipline, such as high-dimensional statistical inference, dimensionality reduction, data mining, machine learning, and bioinformatics.Learn More
Dr. Edoardo M. Airoldi is the Millard E. Gladfelter Professor of Statistics and Data Science. He also serves as Director of the Fox School’s Data Science Center.
Airoldi joins the Fox School from Harvard University, where he had served since 2009 as a full-time faculty member in the Department of Statistics. He founded and directed the Harvard Laboratory for Applied Statistics & Data Science, until 2017. Additionally, he held visiting positions at MIT and Yale University, and served as a research associate at Princeton University.
A distinguished researcher, Airoldi has authored more than 140 publications and earned more than 12,000 citations. His work focuses on statistical theory and methods for designing and analyzing experiments on large networks and, more generally, modeling and inferential issues that arise in analyses that leverage network data.
His work has appeared in journals across statistics, computer science, and general science, including Annals of Statistics, Journal of the American Statistical Association, Journal of Machine Learning Research, Proceedings of the National Academy of Sciences, and Nature. He has received a Sloan Fellowship, the Shutzer Fellowship from the Radcliffe Institute of Advanced Studies, an NSF CAREER Award, and an ONR Young Investigator Program Award, among others. He has delivered a plenary talk at the National Academy of Sciences Sackler Colloquium on “Causal Inference and Big Data,” in 2015, and he has given an IMS Medallion Lecture at the Joint Statistical Meetings, in 2017.
Airoldi earned his PhD in Computer Science from Carnegie Mellon University, where he also received his Master of Science degrees in Statistics and Statistical and Computational Learning. He earned a Bachelor of Science in Mathematical Statistics and Economics from Italy’s Bocconi University.
|Research Interests:||Clinical trials, Group sequential, Design of experiments|
Dr. Alexandra Carides comes to the Fox School with 20 years of pharmaceutical industry experience in applied statistics at Merck and Co.
As Merck’s Associate Director of Scientific Staff, Dr. Carides led teams of scientists through the process of designing studies, analyzing data and writing clinical study reports – 50 of which have been published in peer-reviewed scientific journals. Dr. Carides collaborated with clinical experts, and marketing groups, to help develop and promote Merck products. Along with her career at Merck, Dr. Carides kept current with the academic environment by teaching as an Adjunct in the Fox School’s Department of Statistics.
Before beginning her career at Merck, Dr. Carides worked for 10 years in Romania, first as a mathematics instructor and then as a researcher in Bucharest’s Center of Mathematical Statistics. Dr. Carides earned a Master of Science in Mathematics at the University of Bucharest, Romania, where she was class valedictorian, and her PhD in Statistics from the Fox School.
- MS Aapro, HJ Schmoll, F Jahn, AD Carides, RT Webb (2013) Review of the efficacy of aprepitant for the prevention of chemotherapy-induced nausea and vomiting in a range of tumor types, Cancer Treatment Reviews 39 (1), pp 113-117.
- Adiga, R., Carides, A., Chitturi, P. Changes in PINCH and hpTau levels in the CSF of HIV patients. Journal of NeuroVirology, May 2014.
- A Molassiotis, AM Nguyen, CN Rittenberg, A Makalinao, A Carides (2013). Analysis of aprepitant for prevention of chemotherapy-induced nausea and vomiting with moderately and highly emetogenic chemotherapy. Future Oncology (No. 10), pp 1443-1450.
- Hesketh PJ, Warr DG, Street JC, Carides AD. “Differential time course of action of 5-HT3 and NK1 receptor antagonists when used with highly and moderately emetogenic chemotherapy (HEC and MEC)” Supportive Care Cancer, 2011.
- Carides A, Reiss T, Lines C, McKeon B, Diemunsch P. “Comparisons of aprepitant and ondansetron”, Am J Health Syst Pharm, 2007.
- Calculus and Pre-calculus for Business (undergraduate, online)
- Design of Experiments and Quality Control (undergraduate)
- Statistics for Business (undergraduate)
- Invited to The Aspen Forum for Healthcare – Bucharest, Romania—interview published in local media February 2013
|Research Interests:||Choice Based Conjoint Analysis, Experimental Design, Quality Assurance|
Pallavi Chitturi is a Research Professor in the Department of Statistics and Director of the Center for Statistical Analysis. Dr. Chitturi teaches statistics courses at The Fox School of Business and for the EMBA programs in Philadelphia and Cali, Colombia. She also teaches for the Executive Doctorate in Business Administration (EDBA) program at Fox, and serves as the Associate Academic Director of the program. She has taught for the Study Abroad Program at Temple University – Rome.
Dr. Chitturi’s research interests are in the areas of choice based conjoint analysis, experimental design, and quality assurance. Dr. Chitturi has made several research presentations at national and international conferences, and has published articles in statistics and quality management journals. Dr. Chitturi has successfully supervised Ph.D. dissertations and published a book titled ‘Choice Based Conjoint Analysis – Models and Designs’.
Dr. Chitturi is a recipient of the Lindback Distinguished Teaching Award, the Andrisani-Frank Undergraduate Teaching Award, and the Crystal Apple Teaching Award. She was named a Dean’s Teaching Fellow for innovation in teaching and excellence in the classroom. Dr. Chitturi was awarded 3 grants from the U.S. Navy’s Naval Logistics Readiness Research Center and one grant from the Office of The Vice-Provost for Research at Temple University.
|Research Interests:||Sufficient dimension reduction, High-dimensional inference, Machine learning and data mining|
Yuexiao Dong has joined the Fox School as an Assistant Professor in the Statistics Department. Dr. Dong received his Bachelor’s degree in mathematics from Tsinghua University. He obtained his PhD from the statistics department at the Pennsylvania State University in 2009.
Dr. Dong’s research focuses on sufficient dimension reduction and high-dimensional data analysis. His research articles have been published in top-tier journals such as Annals of Statistics and Biometrika. His proposal “New Developments in Sufficient Dimension Reduction” has been funded by National Science Foundation.
- Li, B. and Dong, Y. (2009) Dimension Reduction for Non-Elliptically Distributed Predictors. Annals of Statistics, 37, 1272-1298.
- Dong, Y. and Li, B. (2010) Dimension Reduction for Non-Elliptically Distributed Predictors: Second-Order Methods. Biometrika, 97, 279-294.
- Dong, Y. and Yu, Z. (2012) Dimension Reduction for the Conditional kth Moment via Central Solution Space. Journal of Multivariate Analysis, 112, 207-218.
- Zhu, L. P., Dong, Y., and Li, R. (2013) Semi-Parametric Estimation of Conditional Heteroscedasticity via Single-Index Modeling. Statistica Sinica, 23, 1235-1255.
- Bharadwaj, N. and Dong, Y. (2014) Toward Further Understanding the Market-sensing Capability–Value Creation Relationship. Journal of Product Innovation Management, 31, 799-813.
- Stat 8108: Applied Multivariate Analysis
- Stat 2521: Data Analysis and Statistical Computing
- Stat 8109: Regression and Time Series
- Wilcox, Kieth, Lauren Block and Eric M. Eisenstein (2011), “Leave Home Without It? The Effects of Credit Card Debt and Available Credit on Spending”, Journal of Marketing Research, 48, pp. 60-78.
- Hutchinson, J. Wesley, Joseph W. Alba, and Eric M. Eisenstein (2010), “Managerial Inferences: The Effects of Graphical Formats on Data-Based Decision Making,” Journal of Marketing Research, 47, 4, pp. 627-642.
- Eisenstein, Eric M. (2010), “Consumer Expertise,” Wiley International Encyclopedia of Marketing, John Wiley and Sons, Chichester, West Sussex, England, ISBN 978-1-405-16178-7.
- Eisenstein, Eric M. and J. Wesley Hutchinson (2006), “Action Based Learning: Goals and Attention in the Acquisition of Market Knowledge,” Journal of Marketing Research, 43, 2, pp. 244-258.
- Hutchinson, J. Wesley and Eric M. Eisenstein (2008), “Consumer Learning and Expertise,” in The Handbook of Consumer Psychology, Haugtvedt, Herr, and Kardes, eds., Lawrence Erlbaum Associates, Mahwah, NJ.
- Best Conference Paper: Marketing and Public Policy Conference, 2008
- Dean's Research Honor Roll: 2012-2013
Dr. Gottlieb has earned his PhD and two Masters Degrees from Columbia in Industrial and Management Engineering.
Dr. Gottlieb has been three years in a row, 2014 – 2016 a Microsoft Most Valuable Professional (“MVP”) in the Microsoft MVP Award Program.
Dr. Gottlieb teaches courses and workshops in Excel for Decision Making and Statistics for Managers and Excel for business applications. Over 3,400 students have taken this online class with him over the past 12 months.
Before joining Temple he has been part of the teaching faculty at Rutgers for over 10 years and was the recipient of a number of teaching excellence awards. He was previously the Director of the International Executive MBA program at Rutgers University Business School.
Over the last 15 years, he has taught how to use Excel—and how to apply it effectively to various business disciplines—to thousands of MBA and Executive MBA students at Rutgers, Temple, NYU, Columbia and other universities. He has also instructed Excel application in business settings to over 50,000 participants in corporate seminars, conferences, and workshops. The participants were professionals from leading multinational companies as well as small private business corporations. Among them are Johnson & Johnson, Merck, Pfizer, Procteor & Gamble, Caterpillar, Vornado, El-Al Airlines, Microsoft, Intel, Boeing, NCR, Chrysler, Sealed Air, JPMorgan, Morgan Stanley, 3M, H-P, and the New York City Economic Development Corporation.
Dr. Gottlieb has an Excel-Tip-Of-The-Month newsletter that goes to thousands of recipients. To subscribe you can email him to the above address.
Dr. Gottlieb has published a book Next Generation Excel: Modeling In Excel For Analysts And MBAs (For MS Windows And Mac OS), (Wiley 2013).
He has over more than 20 years of business/ and industry experience as a CEO of two manufacturing companies and as a senior consultant. He was the president of Peliplast, Inc., a plastics injection molding company specializing in computer printer cartridges; and managed a printer-ribbons manufacturing plant, DI-Tech, Inc. Dr. Gottlieb has also worked as an independent consultant and in a consulting ﬁrm on projects including the New York City Fire Department (simulations), Medicaid of NY State (data-mining), Bet-Shemesh Jet Engines (capital budgeting risk analysis) Israeli Aircraft Industries (supply chain management and procurement), Israeli Ministry of Defense (procurement), US DOD (simulations), New York Stock Exchange (data-entry quality control), CBS Records, Ricoh, Digital Corporation (supply chain management) and a number of others.
|Research Interests:||Large Scale Multiple Testing, High Dimensional Variable Selection, Shrinkage Estimation, Causal Inference|
Dr. Xu Han joins the Fox School from the University of Florida, where he served as an Assistant Professor of Statistics.
His research interests focus on high-dimensional statistics, multiple hypothesis testing, statistical decision theory and causal inference. He has published an article in the Annals of Applied Statistics, and an article in the Journal of American Statistical Association, which are both top-tier journals in Statistics.
Prior to his position at the University of Florida, Dr. Han was a Postdoctoral Research Fellow and Lecturer at Princeton University’s Department of Operations Research and Financial Engineering.
Dr. Han has received several honors and awards, including the Laha Award from the Institute of Mathematical Science and the J. Parker Bursk Memorial Prize (awarded for the best student research annually) in the Statistics Department at the University of Pennsylvania’s Wharton School.
Dr. Han received Bachelor of Science in Mathematics from Fudan University in Shanghai, China, and his MS and PhD in Statistics from the Wharton School.
- Fuki, I., Brown, L., Han, X., and Zhao, L. “Hunting for Significance: Bayesian Classifiers under a mixture Loss Function,” Journal of Statistical Planning and Inference, to appear, 2014
- Fan, J., Han, X. and Gu, W. (2012) Estimating False Discovery Proportion Under Arbitrary Covariance Dependence (with discussion). JASA, Theory and Methods, 107(499), 1019-1035.
- Fan, J., Han, X. and Gu, W. (2012) Rejoinder: Estimating False Discovery Proportion Under Arbitrary Covariance Dependence. JASA, Theory and Methods, 107(499), 1046-1048.
- Han, X., Small, D., Foster, D. and Patel, V. (2011) The Effect of Winning an Oscar Award on Survival: Correcting for Healthy Performer Survival Bias with a Rank Preserving Structural Accelerated Failure Time Model. Annals of Applied Statistics, Vol 5, 746-772.
- Laha Award from the Institute of Mathematical Statistics (IMS) 2010
- J. Parker Bursk Memorial Prize (Awarded for the best student research) from the Department of Statistics at the Wharton School, University of Pennsylvania, 2008
- Deming Student Scholar Award from the 64th Deming Conference on Applied Statistics, 2008
Fox School of Business, Temple University
- STAT 8101 – Stochastic Process
- STAT 2512 – Intermediate Statistics
- STAT 4321 – Introduction to Probability
- FIN 505 – Modern Regression and Time Series
Alan J. Izenman is Professor of Statistics and Senior Research Fellow in the Department of Statistics. He received his B.Sc.(Econ.) degree in 1967 from the London School of Economics and his Ph.D. in Statistics in 1972 from the University of California, Berkeley. He has held faculty positions at Tel-Aviv University and Colorado State University, and visiting positions at the University of Chicago, the University of Minnesota, Stanford University, and the University of Edinburgh. During 1992-1994, he was Director of the Statistics and Probability Program at the National Science Foundation. He is a Fellow of the American Statistical Association. His research interests are primarily in multivariate analysis, time series analysis, nonparametric curve estimation, data mining, machine learning, and bioinformatics, and the application of statistical methods to law and public policy.
|Research Interests:||Online Education, Instructional Technology|
Dr. Kapanjie, who joined Fox’s Statistics Department in 2003, is committed to teaching beyond the physical classroom. He is known for using cutting-edge teaching technologies, and was an early adopter of Class Capture, Wireless Tablet Annotation, Pearson’s MyLabs, and he was the first to use WebEx web conferencing tools to teach synchronous online classes.
Dr. Kapanjie serves on numerous curriculum and technology committees and has helped infuse the use of efficient, reliable and user-friendly technologies throughout the school.
While at Fox, he has been honored with Philadelphia Business Journal’s Top 40 Under 40 Award, the Musser Award for Leadership in Teaching, the Andrisani-Frank Excellence in Undergraduate Teaching Award, the Innovation in Teaching Award at Temple’s Classroom Engagement Through Technology Conference, a two-time winner of the Fox Business Honors Teacher of the Year, and was the inaugural recipient of the John DeAngelo Innovative Leader in Technology Award.
- “The Best of Both Worlds: Effectively Integrating Robust Synchronous & Asynchronous Tools in Distance Education”, UB Tech, July 2013.
- “Community of Practice in Online Education: Collaborative Curriculum”, Campus Technology, July 2011.
- “Community of Practice in Online Education: Retain High-Quality Students with a Collaborative Curricular Design”, EduComm, June 2011.
- “How Information Technology Can Assist & Contribute to Internationalizing Business Education”, Community College of Philadelphia, March 2011.
- “Developing a Global Agenda for the Commonwealth: Business, Education & Technology”, Pennsylvania Council for International Education (PaCIE), October 2010.
- “Staying Connected through Web-Conferencing Technology: The New Fox Online MBA”, Campus Technology, July 2010.
- 40 Under 40, Philadelphia Business Journal — 2013
- Online MBA Professor of the Year Award, Fox School of Business — 2012
- Musser Award for Excellence in Teaching, Fox School of Business — 2011
- Business Honors Teacher of the Year, Fox School of Business — 2010, 2009
- Inaugural recipient of the John DeAngelo Excellence in Teaching and Innovation Award, Fox School of Business — 2009
- Dean’s Teaching Fellow, Fox School of Business — 2008
- Andrisani-Frank Undergraduate Teaching Award, Fox School of Business — 2005
- Excellence in Use of Technology, Center for Innovation in Teaching and Learning, Fox School of Business — 2005
Fox School of Business, Temple University
- Quantitative Methods for Business (graduate) – Traditional & Online
- Honors Calculus for Business – Traditional & Hybrid
- Calculus for Business – Traditional, Online & Hybrid
- Precalculus for Business – Traditional, Online & Hybrid
Dr. Vishesh Karwa is an Assistant Professor of Statistical Science.
He joins the Fox School from The Ohio State University, where he served in the Department of Statistics. He previously served two years as a post-doctoral fellow at Harvard University’s Center for Research and Computation for Society and one year at Carnegie Mellon University.
His research interests include data privacy; causal inference under network interference; social network models; algebraic statistics and computational methods for intractable likelihoods, among others.
Karwa earned his PhD in Statistics from The Pennsylvania State University, where he also attained a Master of Science in Transportation Engineering. He received a Bachelor of Technology in Civil and Environmental Engineering from the Indian Institute of Technology.
Dr. Kuang-Yao Lee joins the Fox School on a tenure-track appointment within the Department of Statistical Science.
Prior to his arrival, Lee served as an associate research scientist at Yale University’s Center for Statistical Genomics and Proteomics, where he investigated and developed statistical methods for high-dimension data and conducted collaborative research.
His current research interests span across two disciplines — statistical genomics and machine learning with high dimensionality as a common theme. Much of his work has been published in top journals with applications to various fields, such as bioinformatics, image analysis, and functional data. He also works closely with biologists and computer scientists on problems related to genome-wide association studies, gene regulatory network, and pathway analysis.
Lee received his PhD in Statistics from The Pennsylvania State University. He earned a Master of Science degree and a Bachelor of Science degree, both in Mathematics, from National Taiwan University.
|Research Interests:||Nonparametric Data Science and United Statistical Algorithms|
Dr. Mukhopadhyay works in both theoretical and applied side of Statistical data science. The ultimate goal of
his research is to develop a unified Statistical theory for data analysis that can lead to polyefficient and
versatile algorithms. Keeping the end in mind, he has launched a new and exciting discipline–“Nonparametric
Data Science” for progressive unification of fundamental statistical learning tools. Under this new framework,
significant number of statistical problems have been tackled to date, including: statistical spectral graph
analysis, large-scale mode identification for discovery science, unified multiple testing, nonparametric copula
dependence modelling, non-linear time series modelling, high-dimensional k-sample modelling, generalized
empirical Bayes modelling, and nonparametric distributed learning for massive data.
- Mukhopadhyay, S. and Fletcher, D. (2018) Bayesian Modeling via Frequentist Goodness-of-fit. Nature Scientific Reports.
- Bruce, S., Li, Z., Yang, H., and Mukhopadhyay, S. (2018) Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Transactions on Big
- Mukhopadhyay, S. (2017) Large-Scale Mode Identification and Data-Driven Sciences. Electronic Journal of Statistics.
- Mukhopadhyay, S. and Nandi, S (2017) LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification. Machine Learning.
- Mukhopadhyay, S. (2016) Large-Scale Signal Detection: A Unifying View. Biometrics
- Parzen, E. and Mukhopadhyay, S. (2013) United Statistical Algorithms, LP comoment, Copula Density, Nonparametric Modeling. 59th ISI World Statistics Congress (WSC), Hong Kong.
- Lahiri, S.N. and Mukhopadhyay, S. (2012) Penalized Empirical Likelihood method for High Dimension. Annals of Statistics, 5, 2511-2540.
- 2016: Best Paper Award, JSM ASA Section on Nonparametric Statistics
- 2016: Best Paper Award, JSM ASA Section on Statistical Computing
- 2014: Winner IEEE International Biometric Eye Movements Verification and Identification Competition.
- 2011: Best presentation by Google at SIAM International Conference on Data Mining
- 2010: Best Student Paper, Section on Nonparametric Statistics of ASA, JSM, Vancouver, Canada.
Fox School of Business, Temple University
- Stat 8001, Probability & Stat Theory I
- Stat 8002, Probability & Stat Theory II
- Stat 2103, Business Statistics
Dr. Pred is Associate Professor of Instruction in the Department of Statistical Science, and Academic Director of the Fox Business Honors Program. He teaches traditional and online BBA, honors, and general education courses, and has taught in the Fox MBA and Executive MBA programs. He is a three-time recipient of the “Fox Honors Instructor of the Year Award,” and received the “Outstanding Service Award for Commitment to Students and Staff of the Fox Honors Program” for his service as a Business Honors Faculty Fellow and his support of the Alter Research Scholars Program. Dr. Pred also received the “Andrisani-Frank Outstanding Teaching & Teaching Related Service Award.” He was named a founding member of the “Provost’s Undergraduate Mentors” for his support of the Diamond Peer Teacher Internship program, and conducts workshops for the Diamond Peer Teacher Internship orientation program. He was the first Faculty Fellow of Temple’s Teaching and Learning Center (TLC), providing mentoring services and conducting numerous faculty development workshops. As TLC Senior Faculty Fellow, he co-taught the Provost’s Teaching Academy for several years. Dr. Pred’s research on the psycho-social correlates of performance, achievement motivation and health appears in the Journal of Allied Health, Journal of Applied Psychology, and Personality and Social Psychology Bulletin. Before joining Temple University, he served as a healthcare marketing research analyst at area firms, including The Vanderveer Group (TVG), and National Analysts (NA) Division of Booz, Allen, & Hamilton. Robert holds a B.A. in Psychology and a Ph.D. in Social Psychology from the University of Texas at Austin, and an M.A. in Industrial and Organizational Psychology from Bowling Green State University of Ohio.
Dr. Donald B. Rubin is the Murray Shusterman Senior Research Fellow.
He joins the Fox School from Harvard University, where he was the John L. Loeb Professor of Statistics. He had served on Harvard’s faculty as full professor of Statistics since 1983, chairing its Department of Statistics for 13 of those years.
As of 2017, Rubin had authored or co-authored more than 400 publications, included 10 books. He holds four joint patents and is considered one of the most highly-cited authors in the world, having earned nearly 250,000 citations according to Google Scholar, with nine singly authored ones with more than a 1,000 citations each. He holds honorary doctorate degrees from six universities in three continents, and honorary professorships from five universities in five continents.
Rubin’s research interests include the causal inference in experiments and observational studies; inference in sample surveys with nonresponse and in missing data problems; application of Bayesian and empirical Bayesian techniques; and developing and applying statistical models to data in a variety of scientific disciplines.
He is an elected fellow, member, or honorary member of: US National Academy of Sciences, The American Academy of Arts and Sciences, the British Academy, the Woodrow Wilson Society; the Guggenheim Memorial Foundation; the Alexander von Humboldt Foundation; the American Statistical Association; the Institute of Mathematical Statistics; the International Statistical Institute; the American Association for the Advancement of Science, and more.
Rubin earned a PhD in Statistics from Harvard University, where he also attained a Master of Arts in Computer Science. He received a Bachelor of Arts degree in Psychology from Princeton University in 1965.
|Research Interests:||Multiple Testing, Statistical Methodolgies, High-Dimensional Statistical Inference, Multivariate Statistics|
Dr. Sanat K. Sarkar is an internationally recognized researcher who has made fundamental contributions to the development of the field of multiple testing toward its applications in modern scientific investigations, such as in genomics and brain imaging. His research has been funded by the National Science Foundation and the National Security Agency, and often been cited in peer-reviewed journals. He has delivered invited talks at numerous national and international conferences. He co-organized a major conference on Multiple Comparisons funded by the NSF-CBMS and severed on the organizing committees of several international conferences on the same topic. He has served on the editorials boards of several respectable journals, like the Annals of Statistics, the American Statistician, and Sankhya. Dr. Sarkar has been recognized as a fellow by both the Institute of Mathematical Statistics and the American Statistical Association, and as an elected member of the International Statistical Institute. He was awarded the Musser Award for excellence in research by the Fox School and inducted several times to the Dean’s Research Honor Roll.
- Sarkar, S. K. “Some results on false discovery rate in stepwise multiple testing procedures,” The Annals of Statistics, 30 (1) 239-257, 2002.
- Sarkar, S. K. and Chang, CK. “The Simes method for multiple hypothesis testing with positively dependent test statistics, ”Journal of the American Statistical Association 92 (440), 1601-1608, 1997
- Sarkar, S. K. “Some probability inequalities for ordered MTP2 random variables: a proof of the Simes conjecture, ”The Annals of Statistics, 26 (2), 494-504, 1998.
- Sarkar, S. K. “False discovery and false nondiscovery rates in single-step multiple testing procedures,”The Annals of Statistics, 394-415, 2006.
- Gavrilov, Benjamini, and Sarkar, “An adaptive step-down procedure with proven FDR control under independence,” The Annals of Statistics, 619-629, 2009.
- Fellow, Institute of Mathematical Statistics, and American Statistical Association
- Elected Member, International Statistical Institute
- The Musser Award for Excellence in Leadership for Research, Fox School of Business Management, Temple University, 2000
Fox School of Business, Temple University
- Advanced Statistical Inference I
- Advanced Statistical Inference II
- Modern Multiple Testing Methods
Lauren Burns is an Assistant Professor of Instruction in the Department of Statistical Science. She teaches traditional and online BBA, honors, and general education courses, as well as courses in the Fox MBA and Executive MBA programs. Outside of teaching, Dr. Burns has also worked on various consulting projects for the Center for Statistical Analysis at Temple.
Dr. Burns’ research interests are in the areas of survival analysis, specifically the presence of non-proportional hazards, high-dimensional data, and dimension reduction. She focuses mostly on applications in Biostatistics dealing with micro-array gene expression data.
Before beginning her career here, Dr. Burns received her M.S. degree and PhD in Statistics from Temple’s Fox School of Business. She also studied at Muhlenberg College, where she earned a Bachelor of Science degree in Mathematics and Economics.
Dr. Cheng Yong Tang joins the Fox School following his appointment as an Assistant Professor of Business Analytics at the Business School of the University of Colorado, Denver. Dr. Tang previously taught at the National University of Singapore, as a tenured professor in the Department of Statistics and Applied Probability. His research interests include high-dimensional data analysis, econometrics, analysis of missing data, and nonparametric statistical methods. Dr. Tang has published 18 research articles, with three more articles in various review stages.
Dr. Tang has received external grants to support his research and travel. He is the recipient of multiple honors, including the National University of Singapore’s Young Scientist Award and Teaching Excellence Award, as well as Iowa State University’s Research Excellence and Teaching Excellence Awards, respectively. Dr. Tang, an elected member of the International Statistical Institute, obtained a Bachelor’s of Science in Management Science and Bachelor’s of Engineering in Computer Science from the University of Science and Technology in China. He earned his Master’s degree in Statistics from the National University of Singapore. He also received his PhD in Statistics from Iowa State University.
- Liu, C, Tang, CY. (2014). A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data. Journal of Econometrics.
- Zhang, W., Leng, C., Tang, CY. (2014). A joint modeling approach for longitudinal studies. Journal of the Royal Statistical Society, Series B.
- Tang, CY, WU, TT. (2014). Nested coordinate descent algorithms for empirical likelihood. Journal of Statistical Computation and Simulation.
- Chang, J, Tang, CY, Wu, Y. (2013). Marginal empirical likelihood and sure independence screening. Annals of Statistics. 41 2132-2148.
- Liu, C, Tang, CY. (2013). A state space model approach to integrated covariance matrix estimation with high frequency data. Statistics and Its Interface (Special FERM2012 Issue, invited article). 6 463-475.
- YUMPS travel grant, University of Colorado Denver (2013)
- Student Scholarship for Statistics Spring Research Conference 2007 (SRC 2007)
- Graduate Students Tuition Awards, Iowa State University
- Student Travel Award for JSM 2006 (ASA Section on Survey Research Methods)
- Professional Advancement Grants (Travel Support), Iowa State University, (2005, 2006, 2007, 2008)
- Research Scholarship, National University of Singapore, 2001-2003
- Outstanding Undergraduate Student Scholarships, University of Science and Technology of China (1996, 1997)
- First Class Award, National High School Mathematics Contest, China (1995)
University of Colorado, Denver
- DSCI 6828 Data Mining: Predictive Modeling
- BUSN 6530 Data Analysis for Managers
Reza Vafa joins the Fox School faculty after serving as an adjunct faculty member in the Department of Statistical Science.
Vafa brings a wealth of statistical and analytical expertise. He earned previous academic experience in adjunct appointments at West Chester University and Delaware County Community College.
He received his Master of Science degree in Applied Statistics from West Chester University. He also completed coursework towards a Master of Science degree in Statistics from Isfahan University, and earned a Bachelor of Science degree in Statistics from Shahid Beheshti University – both in Iran.
|Research Interests:||aggregation effects, time unit selection, multivariate GARCH, repeated measurements, space-time modelling, and high dimension reduction in multivariate time series analysis|
Dr. Wei is Professor of Statistics and former Director of Graduate Programs in Statistics at Temple University in Philadelphia, PA. He has been on the faculty since 1974. He earned his B.A. in Economics from the National Taiwan University (1966), B.A. in Mathematics from the University of Oregon (1969), and M.S. (1972) and Ph.D. (1974) in Statistics from the University of Wisconsin, Madison. From 1982-87, he was the Chair of the Department of Statistics at Temple University. He has been a Visiting Professor at many universities including Nankai University in China, National University of Colombia in Colombia, National Sun Yat-Sen University, National Chiao Tung University, and National Taiwan University in Taiwan. His research interests include time series analysis, forecasting methods, statistical modeling, and their applications. He has developed new methodology in seasonal adjustment, aggregation and disaggregation, outlier detection, robust estimation, and multivariate time series analysis. Some of his most significant contributions include extensive research on the effects of aggregation, methods of measuring information loss due to aggregation, new stochastic procedures of performing data disaggregation, model-free outlier detection techniques, robust methods of estimating autocorrelations, statistics for analyzing multivariate time series, and dimension reduction for high dimensional time series. His first book,Time Series Analysis–Univariate and Multivariate Methods, the first edition published in 1990 and the second edition published in 2006, has been translated into several languages and heavily cited by researchers worldwide. He has just completed his second book,Multivariate Time Series Analysis and Applications. He is an active educator and researcher. He has successfully supervised many Ph.D. students, who hold teaching positions at universities or leadership positions in government and industry throughout the world. He is a Fellow of the American Statistical Association, a Fellow of the Royal Statistical Society, and an Elected Member of the International Statistical Institute. He was the 2002 President of ICSA (International Chinese Statistical Association). He is currently an Associate Editor of theJournal of Forecasting and theJournal of Applied Statistical Science. In addition to teaching and research, he is also active in community service. He served on the educational advisory committee of local school districts, as the chair of the selection committee for a community high school scholarship program, and as the president of several community organizations, including the Taiwanese Hakka Associations of America. Among the many awards he has received are the 2014 Lifetime Achievement Award and the 2016 Musser Award for Excellence in Research from the Temple University Fox School of Business.
- Time Series Analysis: Univariate and Multivariate Methods, 1990
Addison-Wesley, Reading, Massachusetts.
- Time Series Analysis: Univariate and Multivariate Methods, 2nd edition, 2006
Addison-Wesley, Reading, Massachusetts.
- Multivariate Time Series Analysis and Applications, Wiley, forthcoming.
- Effect of Temporal Aggregation on Dynamic Relationships of Two Time Series Variables (with G.C. Tiao), 1976, Biometrika, 63 (3), 513-523.
- Effect of Temporal Aggregation on Parameter Estimation in the Distributed Lag Model, 1978, Journal of Econometrics, 8, 237-246.
- Some Consequences of Temporal Aggregation in Seasonal Time Series Models, 1978, Seasonal Analysis of Economic Time Series, ed. A. Zellner, 433 444.
- Effect of Systematic Sampling on ARIMA Models, 1981, Communications in Statistics, A10 (23), 2389-2398.
- The Effect of Systematic Sampling and Temporal Aggregation on Causality A Cautionary Note, 1982, Journal of the American Statistical Association, 77 (378), 316-319.
- Modeling the Advertising-Sales Relationship Through Use of Multiple Time Series Techniques (with Joseph F. Heyse), 1985, Journal of Forecasting, 4, 165-181.
- Temporal Aggregation in the ARIMA Process (with Daniel Stram), 1986, Journal of Time Series Analysis, 7, 279-292.
- Seasonal Adjustment of Time Series Using One Sided Filters (with Leonard Cupingood), 1986, Journal of Business and Economic Statistics, 4 (4), 473-484.
- A Methodological Note on the Disaggregation of Time Series Totals (with Daniel Stram), 1986, Journal of Time Series Analysis, 7 (4), 293-302.
- An Eigenvalue Approach to the Limiting Behavior of Time Series Aggregates (with D. Stram), 1988, Annals of the Institute of Statistical Mathematics, 40 (1), 101-110.
- Disaggregation of Time Series Models (with D. Stram), 1990, Journal of the Royal Statistical Society B, 52 (3), 453-467.
- A Comparison of Some Estimates of Time Series Autocorrelations (with W. Chan), 1992, Computational Statistics and Data Analysis, 14, 149-163.
- On Likelihood Distance for Outlier Detection (with S. Chow and W. Wang), 1995, Journal of Biopharmaceutical Statistics, 5, 307-322.
- The Effects of Temporal Aggregation on Tests of Linearity of a Time Serie (with P. Teles), 2000, Computational Statistics & Data Analysis, 34, 91-103.
- The Use of Aggregate Time Series in Testing for Gaussianity (with P. Teles), 2002, Journal of Time Series Analysis, 23, 95-116.
- Testing a Unit Root Based on Aggregate Time Series (with P. Teles and E. Hodgess), 2008, Communications in Statistics-Theory and Methods, 37, 565-590.
- Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models (with C. Yozgatligil), 2009, The American Statistician, 63 (4), 328-334.
- Weighted Scatter Estimation Method of the GO-GARCH Models (with L. Zheng), 2012, Journal of Time Series Analysis, 33, 82-95.
- The Use of Temporally Aggregated Data on Detecting a Mean Change of a Time Series Process (with B.Y. Lee). 2017, Communications in Statistics-Theory and Methods, 46(12), 5851-5871.
- Fellow of the Royal Statistical Society
- Elected Member of the International Statistical Institute
- Fellow of the American Statistical Association
- 2002 ICSA President
- 2014 Fox School Lifetime Achievement Award
- 2016 Fox School Musser Excellence in Leadership Award for Research
Fox School of Business, Temple University
- Quantitative Methods for Business
- Statistical Business Analytics
- Probability and Statistics Theory
- Mathematics for Statistics
- Univariate Time Series Analysis
- Multivariate Time Series Analysis
- Applied Multivariate Analysis
- Regression, Time Series, and Forecasting for Business Applications
- Time Series Analysis and Forecasting
Dr. Gary Witt returns to the Fox School on a non-tenure-track appointment within the Department of Statistical Science. He previously served the Finance and Statistics departments as a full-time non-tenure track faculty member from 2008-2015, before accepting a similar position at Syracuse University.
Prior to his pedagogical career, Witt pursued a 20-year career in finance in New York and London in derivatives, structured finance, and investment management, which includes a term as managing director at Moody’s Investors Service. He drew from his many years of experience to teach statistics and finance at the Fox School, where he worked to incorporate tenets of environmental sustainability into his business curriculum.
Witt received his PhD in Statistics from the Wharton School at the University of Pennsylvania. He also studied at Southern Methodist University, where he earned a Bachelor of Science degree in Economics and Bachelor of Arts degrees in Mathematics and Physics.
|Research Interests:||Bayes/empirical Bayesian statistics, Multiple comparison, bioinformatics, High dimensional statistical inference|
Zhigen Zhao graduated from Cornell University in 2009. Dr. Zhao’s research interests include Bayesian/empirical Bayesian statistics, high dimensional data analysis, multiple comparison, bioinformatics, selective confidence intervals. Dr. Zhao has published papers in top tier journals, such as Journal of the Royal Statistical Society, Series B, Journal of the American Statistical Association. Dr Zhao’s current research is supported by national science foundation.
- Ji, P. and Zhao, Z. Rate optimal multiple testing procedure in high-dimensional regression. Submitted.
- Zhao, Z. and Sarkar, K. (2012) A Bayesian approach to construct multiple confidence intervals of selected parameters with sparse signals. In press. Statistica Sinica.
- Hwang, J. T. and Zhao, Z. (2013) Empirical Bayes confidence intervals for selected parameters in high dimensional data. Journal of the American Statistical Association. Vol. 108, No. 502, 607-618.
- Zhao, Z. and Hwang, J. T. (2012) Empirical Bayes false coverate rate controlling confidence interval. Journal of the Royal Statistical Society, Series B. 74, 871-891.
- Hwang, J. T., Qiu, J. and Zhao, Z. (2009) Empirical Bayes confidence intervals shrinking both means and variances. Journal of the Royal Statistical Society, Series B, 71, 265-285.
- NSF grant DMS1208735
- Raghavarao Publication Award, Temple University, 2014
- Laha Travel Award, the Institute of Mathematics Statistics, 2009
Fox School of Business, Temple University
- Statistical Methods for Business Research II
- Statistics Methods I/II