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
|Office:||Speakman Hall 345|
|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
|Office:||Speakman Hall 341|
|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.
|Office:||Speakman Hall 348|
|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
|Office:||Alter Hall 527|
Dr. Eisenstein’s research primarily explores how people make decisions that materially affect their lives. These decisions may be financial, such as the purchase of an expensive durable good, or policy oriented, such as interpreting statistical evidence, choosing between alternative proposals, or combating identity theft. Prior to becoming an academic, Dr. Eisenstein worked at Mercer Management Consulting (now Oliver Wyman) where he focused on management of technology and consumer research in the financial services and telecommunications industries. He is active in the community, having founded or led several organizations, and he serves on the board of the Visiting Nurses Association of Philadelphia.
Dr. Eisenstein earned his Ph.D. in Applied Economics and an M.A. in Statistics at the Wharton School of Business at the University of Pennsylvania. He also completed his undergraduate education at Penn in the Management and Technology program, wherein he concurrently earned B.S. degrees from Wharton and the School of Engineering.
- 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
|Office:||Speakman Hall 339|
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.
|Office:||Speakman Hall 328|
|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
|Office:||Speakman Hall 205D|
|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 as the Fox School’s Managing Director of Online & Digital Learning and is the Academic Director of the Fox Online MBA and BBA Programs. He 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
|Office:||Alter Hall 329|
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.
|Office:||Speakman Hall 333|
Dr. Martina Mincheva joins the faculty from Princeton University, where she served as an instructor. Dr. Mincheva’s research focuses on high-dimensional statistical inference, robust statistics, financial econometrics and time series analysis. She has published five research papers in top tier journals, including the Journal of the Royal Statistical Society, Series B and the Annals of Statistics, among others. She also has one paper in preparation. A Princeton University Gordon Wu Fellow in Engineering, she has delivered presentations at conferences and seminars throughout North America. Dr. Mincheva received her Bachelor’s degrees in both Mathematics and Economics from Franklin & Marshall College, her Master’s degree in Operations Research and Information Engineering from Cornell University and her PhD in Statistics from Princeton University.
- Consistent covariance matrix estimation in sparse semiparametric factor models. in preparation. (with Fan, J. and Liao, Y.)
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements. Journal of the Royal Statistical Society: Series B,(2013) 75, Part 4, pp. 603-680 (discussion paper)(with Fan, J., Liao, Y.)
- High-dimensional Covariance Matrix Estimation in Approximate Factor Models. Annals of Statis- tics,(2011) Vol.39, No.6, pp. 3320-3356.(with Fan, J., Liao, Y.)
- Symmetry of Iteration Graphs. Czechoslovak Mathematical Journal,(2008) Vol. 58, No. 1, pp.131-145.(with Carlip, W.)
- Component Growth of Iteration Graphs under the Squaring Map Modulo pk. Fibonacci Quarterly, (2007) Vol.45.3, pp.239-246. (with Carlip, W.)
- Princeton University Gordon Wu Fellowship in Engineering
- Princeton University Deans Fund for Scholarly Travel
- Best Poster Award – International Workshop on the Perspective on High-Dimensional Data Analysis, Pacic Institute for the Mathematical Sciences
- Cornell University Thanks to Scandinavia Scholar – one-year-long fellowship for doctorate studies
- Ronald K. Stuart Scholar for Mathematical Excellence- awarded to only one graduating senior
- Gold medal – Australian Mathematics Competition for the Westpac Awards (2000)
- 2 Gold Medals – Balkan Mathematical Olympiad (1999, 2000)
- Member of the Bulgarian national Mathematics team (1999,2000)
- ORF245 – Fundamentals of Engineering Statistics (Fall 2012, Spring 2013)
- ORF524 – Statistical Theory and Methods (Fall 2012)
- ORF405 – Regression and Applied Time Series (Fall 2011)
- ORF335 – Introduction to Financial Mathematics (Spring 2011, Spring 2012)
- ORF409 – Introduction to Monte Carlo Simulations (Fall 2010)
- ORF411 – Operations and Information Engineering (Fall 2010)
|Office:||Alter Hall 335|
|Research Interests:||LP Nonparametric Data Science; United Statistical Algorithm; Small and Big Data Modeling|
Dr. Subhadeep (Deep) Mukhopadhyay joins the faculty from Texas A&M University. His research – which has applications to Science, Engineering and Business – focuses on building a coherent framework for unified algorithms of data science applicable for both Small and Big Data with increasingly complex structures: univariate, bivariate, time series, spatial, and high dimension. He is also interested in developing timely course curriculum to address the Big Data talent gap.
Dr. Mukhopadhyay has published in the A-level journal Annals of Statistics and has several working papers. His 10 invited presentations include those at Merck Research Laboratories, Bell Laboratories, Michigan State University and the Harvard School of Public Health. Dr. Mukhopadhyay’s honors include Best Student Paper Awards from the Section on Nonparametric Statistics of the American Statistical Association and Best Poster and Presentation honor by Google at the SIAM (Society for Industrial and Applied Mathematics) International Conference on Data Mining. He received his Master’s degree in Statistics from the Indian Institute of Technology (IIT Kanpur) and his Bachelor’s of Statistics from the University of Calcutta.
- Mukhopadhyay, S. and Parzen, E. (2014) LP Approach to Statistical Modeling.
- Parzen, E. and Mukhopadhyay, S. (2013) United Statistical Algorithms, LP Comoment,Copula Density, Nonparametric Modeling. 59th ISI World
Statistics Congress (WSC), Hong.
- Lahiri, S.N. and Mukhopadhyay, S. (2012) On the Mahalanobis-distance based Penalized Empirical Likelihood Method in High Dimensions. Statistics and Its Interface, 5, 331-338.
- Lahiri, S.N. and Mukhopadhyay, S. (2012) Penalized Empirical Likelihood method for High Dimension. Annals of Statistics, 5, 2511-2540.
- 2014: Winner IEEE International Biometric Eye Movements Verification and Identification Competition.
- 2010: Best Student Paper, Section on Nonparametric Statistics of ASA, JSM, Vancouver, Canada.
- 2008: Sangeeta Pradhan Memorial Gold Medal: Outstanding all-round achievement among all disciplines of the Master of Science Programs in IIT Kanpur.
Fox School of Business, Temple University
- Stat 8001, Probability & Stat Theory I
- Stat 8002, Probability & Stat Theory II
- Stat 2103, Business Statistics
|Office:||Speakman Hall 347|
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.
|Office:||Speakman Hall 331|
|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
|Office:||Speakman Hall 337|
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
|Office:||Speakman Hall 336|
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.
|Office:||Speakman Hall 330|
|Research Interests:||aggregation effects, time unit selection, multivariate GARCH, repeated measurements, and space-time modelling|
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 was Visiting Professor of Nankai University in 1988-1989, Visiting Professor of National Sun Yat-Sen University in 1990, Visiting professor of National University of Colombia, in Bogota, Colombia in 1994, Visiting Professor of National Chiao Tung University and Visiting Professor of National Taiwan University in 2010-2011. His research interest includes time series analysis, forecasting methods, statistical modeling, high dimension reduction, and applications of statistics in business and economics. He has developed new methodology in seasonal adjustment, aggregation and disaggregation, outlier detection, robust estimation, and vector 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, and statistics for analyzing multivariate time series. He has successfully supervised many Ph.D. students. Most of his Ph.D. advisees found teaching positions in universities when graduating from Temple. His book, Time Series Analysis-Univariate and Multivariate Methods, has been translated in other languages and heavily cited by researchers worldwide. He is a Fellow of the ASA (American Statistical Association), a Fellow of the RSS (Royal Statistical Society), an Elected Member of the ISI (International Statistics Institute), the 2002 President of ICSA (International Chinese Statistical Association), and the 2014 and 2015 President of Taiwanese Hakka Associations of America. He is the 2014 Lifetime Achievement Award Recipient and the Recipient of the 2016 Musser Award for Excellence in Research of Temple University Fox School of Business. He is an Associate Editor of the Journal of Forecasting and the Journal of Applied Statistical Science.
- 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.
- 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 SeriesProcess (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
- Time Series Analysis I
- Time Series Analysis II
- Applied Multivariate Analysis
- Regression, Time Series, and Forecasting for Business Applications
- Time Series Analysis and Forecasting
|Office:||Alter Hall 334|
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.
|Office:||Speakman Hall 342|
|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