IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v82y2014i2p161-183.html
   My bibliography  Save this article

A Conversation with James O. Ramsay

Author

Listed:
  • Christian Genest
  • Johanna G. Nešlehová

Abstract

type="main" xml:id="insr12053-abs-0001"> Jim Ramsay was born on September 5, 1942, in Prince George, British Columbia. He pursued undergraduate studies at the University of Alberta, where he completed a BEd in 1964 with a major in English and a minor in mathematics. He then specialized in statistics and psychometry, earning a PhD in psychology from Princeton University in 1966. After holding a temporary lectureship in the Department of Psychology at University College London for one year, he joined the Department of Psychology at McGill University, where he rose through the academic ranks. He was chair of his department from 1986 to 1989 and spent sabbatical leaves in Cambridge, Grenoble, and Toulouse. He was named professor emeritus upon his retirement in 2007. Jim is the author of four influential books and over 100 peer-reviewed articles in statistical and psychometric journals. He developed much of the statistical theory behind multidimensional scaling and is widely recognized as the founder of functional data analysis. Three of his papers were read to the Royal Statistical Society, and another won The Canadian Journal of Statistics 2000 Best Paper Award. The Statistical Society of Canada (SSC) awarded him a Gold Medal for research in 1998 and an honorary membership in 2012. Jim was president of the Psychometric Society in 1981–82 and president of the SSC in 2002–03. The following conversation took place at Jim's home in Ottawa, Ontario, on March 14 and April 4, 2012.

Suggested Citation

  • Christian Genest & Johanna G. Nešlehová, 2014. "A Conversation with James O. Ramsay," International Statistical Review, International Statistical Institute, vol. 82(2), pages 161-183, August.
    • Handle: RePEc:bla:istatr:v:82:y:2014:i:2:p:161-183
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/insr.12053
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. Ramsay, 1980. "The joint analysis of direct ratings, pairwise preferences, and dissimilarities," Psychometrika, Springer;The Psychometric Society, vol. 45(2), pages 149-165, June.
    2. J. Ramsay, 1969. "Some statistical considerations in multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 34(2), pages 167-182, June.
    3. J. Ramsay, 1973. "The effect of number of categories in rating scales on precision of estimation of scale values," Psychometrika, Springer;The Psychometric Society, vol. 38(4), pages 513-532, December.
    4. Ramsay, James O. & Ramsey, James B., 2002. "Functional data analysis of the dynamics of the monthly index of nondurable goods production," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 327-344, March.
    5. Theodoro Koulis & James Ramsay & Daniel Levitin, 2008. "From Zero to Sixty: Calibrating Real-Time Responses," Psychometrika, Springer;The Psychometric Society, vol. 73(2), pages 321-339, June.
    6. Natasha Rossi & Xiaohui Wang & James O. Ramsay, 2002. "Nonparametric Item Response Function Estimates with the EM Algorithm," Journal of Educational and Behavioral Statistics, , vol. 27(3), pages 291-317, September.
    7. Stigler, Stephen M., 2010. "The Changing History of Robustness," The American Statistician, American Statistical Association, vol. 64(4), pages 277-281.
    8. Suzanne Winsberg & James Ramsay, 1981. "Analysis of pairwise preference data using integrated B-splines," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 171-186, June.
    9. J. Ramsay, 1989. "A comparison of three simple test theory models," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 487-499, September.
    10. J. Ramsay, 1990. "Matfit: A fortran subroutine for comparing two matrices in a subspace," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 551-553, September.
    11. Kneip, Alois & Ramsay, James O, 2008. "Combining Registration and Fitting for Functional Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1155-1165.
    12. S. Winsberg & J. Ramsay, 1983. "Monotone spline transformations for dimension reduction," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 575-595, December.
    13. Cao, J. & Ramsay, J. O., 2010. "Linear Mixed-Effects Modeling by Parameter Cascading," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 365-374.
    14. Jiguo Cao & James Ramsay, 2007. "Parameter cascades and profiling in functional data analysis," Computational Statistics, Springer, vol. 22(3), pages 335-351, September.
    15. Michal Abrahamowicz & James Ramsay, 1992. "Multicategorical spline model for item response theory," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 5-27, March.
    16. J. Ramsay, 1995. "A similarity-based smoothing approach to nondimensional item analysis," Psychometrika, Springer;The Psychometric Society, vol. 60(3), pages 323-339, September.
    17. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
    18. Cao, Jiguo & Ramsay, James O., 2009. "Generalized profiling estimation for global and adaptive penalized spline smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2550-2562, May.
    19. J. O. Ramsay, 1998. "Estimating smooth monotone functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 365-375.
    20. J. Ramsay, 1980. "Some small sample results for maximum likelihood estimation in multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 45(1), pages 139-144, March.
    21. J. Ramsay & S. Winsberg, 1991. "Maximum marginal likelihood estimation for semiparametric item analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 365-379, September.
    22. J. Ramsay, 1975. "Solving implicit equations in psychometric data analysis," Psychometrika, Springer;The Psychometric Society, vol. 40(3), pages 337-360, September.
    23. Philippe Besse & J. Ramsay, 1986. "Principal components analysis of sampled functions," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 285-311, June.
    24. J. Ramsay, 1978. "Confidence regions for multidimensional scaling analysis," Psychometrika, Springer;The Psychometric Society, vol. 43(2), pages 145-160, June.
    25. Dauxois, J. & Pousse, A. & Romain, Y., 1982. "Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference," Journal of Multivariate Analysis, Elsevier, vol. 12(1), pages 136-154, March.
    26. Laura M. Sangalli & James O. Ramsay & Timothy O. Ramsay, 2013. "Spatial spline regression models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 681-703, September.
    27. Hooker, Giles & Ramsay, James O., 2012. "Learned-loss boosting," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3935-3944.
    28. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    29. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    30. J. Ramsay, 1977. "Maximum likelihood estimation in multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 42(2), pages 241-266, June.
    31. J. Ramsay, 1991. "Kernel smoothing approaches to nonparametric item characteristic curve estimation," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 611-630, December.
    32. J. Ramsay, 1982. "When the data are functions," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 379-396, December.
    33. J. Ramsay, 1977. "Monotonic weighted power transformations to additivity," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 83-109, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bernardi, Mara S. & Carey, Michelle & Ramsay, James O. & Sangalli, Laura M., 2018. "Modeling spatial anisotropy via regression with partial differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 15-30.
    2. Yoshio Takane & Tadashi Shibayama, 1991. "Principal component analysis with external information on both subjects and variables," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 97-120, March.
    3. J. Carroll, 1985. "Review," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 133-140, March.
    4. Xinyu Zhang & Jiguo Cao & Raymond J. Carroll, 2017. "Estimating varying coefficients for partial differential equation models," Biometrics, The International Biometric Society, vol. 73(3), pages 949-959, September.
    5. Dawn Iacobucci & Doug Grisaffe & Wayne DeSarbo, 2017. "Statistical perceptual maps: using confidence region ellipses to enhance the interpretations of brand positions in multidimensional scaling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(3), pages 81-98, December.
    6. S. Winsberg & J. Ramsay, 1983. "Monotone spline transformations for dimension reduction," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 575-595, December.
    7. Phipps Arabie, 1991. "Was euclid an unnecessarily sophisticated psychologist?," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 567-587, December.
    8. Yoshio Takane & J. Carroll, 1981. "Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 389-405, December.
    9. Fang Yao & Yichao Wu & Jialin Zou, 2016. "Probability-enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 1-22, March.
    10. Boente, Graciela & Fraiman, Ricardo, 2000. "Kernel-based functional principal components," Statistics & Probability Letters, Elsevier, vol. 48(4), pages 335-345, July.
    11. Aurea Grané & Rosario Romera, 2018. "On Visualizing Mixed-Type Data," Sociological Methods & Research, , vol. 47(2), pages 207-239, March.
    12. Henry Brady, 1985. "Statistical consistency and hypothesis testing for nonmetric multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 50(4), pages 509-537, December.
    13. Abe, Makoto, 1998. "Error structure and identification condition in maximum likelihood nonmetric multidimensional scaling," European Journal of Operational Research, Elsevier, vol. 111(2), pages 216-227, December.
    14. Saburi, S. & Chino, N., 2008. "A maximum likelihood method for an asymmetric MDS model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4673-4684, June.
    15. Groenen, P.J.F. & Borg, I., 2013. "The Past, Present, and Future of Multidimensional Scaling," Econometric Institute Research Papers EI 2013-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Keith Poole, 1990. "Least squares metric, unidimensional scaling of multivariate linear models," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 123-149, March.
    17. Christoph Hellmayr & Alan E. Gelfand, 2021. "A Partition Dirichlet Process Model for Functional Data Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 30-65, May.
    18. Gert Storms, 1995. "On the robustness of maximum likelihood scaling for violations of the error model," Psychometrika, Springer;The Psychometric Society, vol. 60(2), pages 247-258, June.
    19. Longjuan Liang & Michael W. Browne, 2015. "A Quasi-Parametric Method for Fitting Flexible Item Response Functions," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 5-34, February.
    20. Carey, M. & Ramsay, J.O., 2021. "Fast stable parameter estimation for linear dynamical systems," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:istatr:v:82:y:2014:i:2:p:161-183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.