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William Q Meeker

William Q Meeker

  • Distinguished Professor Emeritus

Contact Info

2109 Snedecor Hall
Social Media and Websites


  • B.S. Clarkson College of Technology (now Clarkson University), 1972, Industrial Management
  • M.S. Union College, 1973, Operations Research
  • Ph.D. Union College, 1975, Administrative and Engineering Systems

More Information

Selected Recent Publications:

  • Meeker, W.Q., G.J. Hahn, and L.A. Escobar, (2017), Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition. John Wiley and Sons, Inc.
  • Mittman, E., C. Lewis-Beck, and W. Q. Meeker (2019), A Hierarchical Failure-Time Model for Observational Data Exhibiting Infant-Mortality and Wearout Failure Modes. Technometrics, 61, 354-368.
  • Tian, Q., S. Liu, and W.Q. Meeker, (2019), Using Degradation Models to Assess Pipeline Life. Applied Stochastic Models in Business and Industry, 35, 1411-1430.
  • Shan, Q., Y. Hong, and W. Q. Meeker, (2020), Seasonal Warranty Prediction Based on Recurrent Event Data. Annals of Applied Statistics, 14, 929-955.
  • Xu, L., C. Gotwalt, Y. Hong, C.B. King, and W.Q. Meeker (2020), Applications of the Fractional-Random-Weight Bootstrap. The American Statistician, 74, 345-358.
  • Doganaksoy, N., W.Q. Meeker,  and G.J. Hahn, (2020), Reliability Disasters: Technical Learnings from Past Mistakes to Mitigate and Avoid Future Catastrophes. Quality Progress, August 2020, 38-44.
  • Doganaksoy, N., W. Q. Meeker, and G.J Hahn (2021), Achieving Product Reliability. CRC Press.
  • Weaver, B. and W.Q. Meeker, (2021) Bayesian Methods for Planning Accelerated Repeated Measures Degradation Tests. Technometrics 63, 90-99.
  • Tian, Q., F. Meng, D.J. Nordman, and W.Q. Meeker (2022), Predicting the Number of Future Events. Journal of the American Statistical Association, 117, 1296-1310.
  • Meeker, W. Q., L. A. Escobar, and F. G. Pascual (2022), Statistical Methods for Reliability Data, Second Edition, Wiley.
  • Tian, Q., Lewis-Beck, C., Niemi, J., and Meeker, W.Q. (2023), Specifying Prior Distributions in Reliability Applications. Applied Stochastic Models in Business and Industry.