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Dr Janet Godolphin

Senior Lecturer
+44 (0)1483 689644
07 AA 04

Academic and research departments

Department of Mathematics, Centre for Mathematical and Computational Biology.

Biography

Biography

I graduated with a BSc degree in Mathematics from Royal Holloway, University of London and gained a PhD in Statistics also from Royal Holloway in 1994. In 2003 I was appointed as a lecturer in the Department of Mathematics at the University of Surrey.

Research interests

  • Design of experiments
  • Residual analysis
  • Statistical process control

My publications

Publications

  • J.D.Godolphin [2019] Construction of row-column factorial designs, Journal of the Royal Statistical Society, Series B,                                          https://doi.org/10.1111/rssb.12305

 

  • D.A. Faux and J.D.Godolphin [2019] Manual timing in physics experiments: error and uncertainty, American Journal of Physics, 87, 110-115.

 

  • J.D.Godolphin [2019] Two-level factorial and fractional factorial replicates in blocks of size two, Computational Statistics and Data Analysis, 133, 120-137.

 

  • J.D.Godolphin [2018] Designs with blocks of size two and application to microarray experiments,The Annals of Statistics, 46, 2775–2805.

 

  • J.D.Godolphin [2018] A note on the robustness of PBIBD(2)s against breakdown in the event of observation loss, Australian and New Zealand Journal of Statistics, 60, 199-208.

 

  • J.D.Godolphin [2016] A link between the E-value and the robustness of block designs, Journal of the American Statistical Association, 111, 1736-1745.

 

  • J.D.Godolphin and E.J. Godolphin [2015] The robustness of resolvable block designs against the loss of whole blocks or replicates, Journal of Statistical Planning and Inference, 163, 34-42.

 

  • J.D.Godolphin and E.J. Godolphin [2015] The use of treatment concurrences to assess robustness of binary block designs against the loss of whole blocks, Australian and New Zealand Journal of Statistics, 57, 225-239.

 

  • J.D.Godolphin and H.R.Warren [2014] An efficient procedure for the avoidance of disconnected incomplete block designs, Computational Statistics and Data Analysis, 71, 1134-1146.

 

  • J.D.Godolphin [2013] On the Connectivity Problem for m-way Designs, Journal of Statistical Theory and Practice, 7, 732-744.

 

  • J.D.Godolphin and H.R.Warren [2011] Improved Conditions for the Robustness of Binary Block Designs Against the Loss of Whole Blocks, Journal of Statistical Planning and Inference, 141, 3498-3505.

 

  • J.D.Godolphin [2009] New formulations for recursive residuals as a diagnostic tool in the classical linear model with arbitrary rank, Computational Statistics and Data Analysis, 53, 2119-2128.

 

  • S.T.Bate, E.J.Godolphin and J.D.Godolphin [2008] Choosing cross-over designs when few subjects are available, Computational Statistics and Data Analysis, 52, 1572-1586.

 

  • E.J.Godolphin and J.D.Godolphin [2007] A note on the information matrix for multiplicative seasonal autoregressive moving average models, Journal of Time Series Analysis, 28, 783-791.

 

  • J.D.Godolphin [2006] The specification of rank reducing observation sets in experimental design,  Computational Statistics and Data Analysis, 51, 1862-1874. 

 

  • J.D.Godolphin [2006] Reducing the impact of missing values in factorial experiments arranged in blocks, Quality and Reliability Engineering International, 22, 669-682.

 

  • K.Triantafyllopoulos, J.D.Godolphin and E.J.Godolphin [2005] Process improvement in the micro-electronic industry by state space modelling, Quality and Reliability Engineering International, 21, 465-475.

 

  • J.D.Godolphin [2004] Simple pilot procedures for the avoidance of disconnected experimental designs, Applied Statistics, 53, 133-147. 

 

  • J.D.Godolphin and E.J.Godolphin [2001] On the connectivity of row-column designs, Utilitas Mathematica, 60, 51-65.