University of Connecticut Department of Statistics

  • Dipak K. Dey
  • Nitis Mukhopadhyay
  • Lynn Kuo
  • Ming-Hui Chen


The Department of Statistics at the University of Connecticut was founded in 1962. As one of the major statistics departments in New England, it provides outstanding preparation for careers in academia, industry, or government. With a core faculty of 15 members whose teaching and research expertise span virtually all major specializations in statistical science, our department has both national and international reputation in undergraduate and graduate education, research, and service to the profession. The department offers BA/BS majors, BA/BS majors in mathematics and statistics jointly, as well as Masters and PhD degrees in statistics.


American Statistical Association Student Fellow Minimum Variance Unbiased Estimation Plenary Speaker Pfizer Global Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Our sincere thanks go to Robert H. Riffenburgh for providing helpful information. We also thank Keith Conrad for sharing the minutes from past UConn Board of Trustee meetings.

References: Selected Books and Papers

Selected Books and Edited Volumes

  1. Chen M-H, Shao Q-M, Ibrahim JG (2000) Monte Carlo methods in Bayesian computation. Springer, New YorkGoogle Scholar
  2. Dey DK, Müller P, Sinha D (eds) (1999) Practical nonparametric and semiparametric Bayesian statistics. Lecture notes series, vol 133. Springer, New YorkGoogle Scholar
  3. Dey DK, Ghosh SK, Mallick BK (eds) (2001) Generalized linear models: a Bayesian perspective. Marcel Dekker, New YorkGoogle Scholar
  4. Glaz J, Naus J, Wallenstein S (2001) Scan statistics. Springer, New YorkGoogle Scholar
  5. Glaz J, Pozdnyakov V, Wallenstein S (eds) (2009) Scan statistics: methods and applications. Birkhauser, BostonGoogle Scholar
  6. Harel O (2009) Strategies for data analysis with two types of missing values: from theory to application. Lambert Academic Publishing, SaarbruckenGoogle Scholar
  7. Ibrahim JG, Chen M-H, Sinha D (2001) Bayesian survival analysis. Springer, New YorkGoogle Scholar
  8. Mukhopadhyay N (2000) Probability and statistical inference. Marcel Dekker (Taylor & Francis Group), New YorkGoogle Scholar
  9. Mukhopadhyay N, Solanky TKS (1994) Multistage selection and ranking procedures: second order asymptotics. Marcel Dekker, New YorkGoogle Scholar
  10. Ravishanker N, Dey DK (2002) A first course in linear model theory. Chapman & Hall, CRC, Boca RatonGoogle Scholar

Selected Articles

  1. Artstein Z, Vitale RA (1975) A strong law of large numbers for random compact sets. Ann Probab 3:879–882Google Scholar
  2. Asgharian M, M’Lan CE, Wolfson DB (2002) Length-based sampling with right censoring. J Am Stat Assoc 97:201–209Google Scholar
  3. Chen M-H (1994) Importance weighted marginal Bayesian posterior density estimation. J Am Stat Assoc 89:818–824Google Scholar
  4. Chen M-H, Ibrahim JG, Sinha D (1999) A new Bayesian model for survival data with a surviving fraction. J Am Stat Assoc 94:909–919Google Scholar
  5. Chi ZY, Geman S (1998) Estimation of probabilistic context-free grammars. Comput Linguist 24:299–305Google Scholar
  6. Dey DK, Srinivasan C (1985) Estimation of a covariance matrix under Stein’s loss. Ann Stat 13:1581–1591Google Scholar
  7. Fine JP, Yan J, Kosorok MR (2004) Temporal process regression. Biometrika 91:683–703Google Scholar
  8. Gelfand A, Dey DK (1994) Bayesian model choice: asymptotics and exact calculations. J R Stat Soc B 56:501–514Google Scholar
  9. Gelfand AE, Kuo L (1991) Nonparametric Bayesian bioassay including ordered polytomous response. Biometrika 78:657–666Google Scholar
  10. Ghosh M, Mukhopadhyay N (1981) Consistency and asymptotic efficiency of two stage and sequential estimation procedures. Sankhyā Ser A 43:220–227Google Scholar
  11. Glaz J (1989) Approximations and bounds for the distribution of the scan statistic. J Am Stat Assoc 84:560–566Google Scholar
  12. Glaz J, Naus J (1991) Tight bounds and approximations for scan statistic probabilities for discrete data. Ann Appl Probab 1:306–318Google Scholar
  13. Harel O, Zhou XH (2006) Multiple imputation for correcting verification bias. Stat Med 25:3769–3786Google Scholar
  14. Harel O, Zhou XH (2007) Multiple imputation review of theory implementation and software. Stat Med 26:3057–3077Google Scholar
  15. Kang S, Cai J (2009) Marginal hazards regression for case-cohort studies with multiple disease outcomes. Biometrika 96:887–901Google Scholar
  16. Kang S, Cai J (2009) Marginal hazards regression for retrospective studies within cohort with possibly correlated failure time data. Biometrics 65:405–414Google Scholar
  17. Kundu S, Majumdar S, Mukherjee K (2000). Central limit theorems revisited. Stat Probab Lett 47:265–275Google Scholar
  18. Kuo L, Yang T (1996) Bayesian computation for nonhomogeneous Poisson process in software reliability. J Am Stat Assoc 91:763–773Google Scholar
  19. Majumdar S (1992) On topological support of Dirichlet prior. Stat Probab Lett 15:385–388Google Scholar
  20. Marriott J, Ravishanker N, Gelfand A, Pai J (1996) Bayesian analysis of ARMA processes: complete sampling-based inference under exact likelihoods. In: Berry DA, Chaloner K, Geweke JK (eds) Bayesian analysis in statistics and econometrics: essays in honor of Arnold Zellner. Wiley, New York, pp 243–256Google Scholar
  21. Mukhopadhyay N, Duggan WT (1997) Can a two-stage procedure enjoy second-order properties? Sankhyā Ser A 59:435–448Google Scholar
  22. Pozdnyakov V, Steele JM (2004) On the martingale framework for futures prices. Stochastic Process Their Appl 109:69–77Google Scholar
  23. Pozdnyakov V, Glaz J, Kulldorff M, Steele JM (2005) A martingale approach to scan statistics. Ann Inst Stat Math 57:21–37Google Scholar
  24. Qiou Z, Ravishanker N, Dey DK (1999) Multivariate survival analysis with positive stable frailties. Biometrics 55:637–644Google Scholar
  25. Stein ML, Chi ZY, Welty LJ (2004) Approximating likelihoods for large spatial data sets. J R Stat Soc B 66:275–296Google Scholar
  26. Villagran A, Huerta G (2006) Bayesian inference on mixture-of-experts for estimation of stochastic volatility. Adv Econometr 20:277–296Google Scholar
  27. Villagran A, Huerta G, Jackson C, Sen M (2008) Computational methods for parameter estimation in climate models. Bayesian Anal 4:823–850Google Scholar
  28. Vitale RA (1979) Approximation of convex set-valued functions. J Approx Theory 26:301–316Google Scholar
  29. Wolfson C, Wolfson D, Asgharian M, M’Lan CE, Ostbye T, Rockwood K, Hogan DB (2001). A reevaluation of the duration of survival after the onset of dementia. New Engl J Med 344:1111–1116Google Scholar
  30. Yan J, Fine JP (2004) Estimating equations for association structures. Stat Med 23:859–874Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Dipak K. Dey
    • 1
  • Nitis Mukhopadhyay
    • 1
  • Lynn Kuo
    • 1
  • Ming-Hui Chen
    • 1
  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA

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