Statistical Science

A Conversation with Emanuel Parzen

H. Joseph Newton

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Emanuel Parzen was born in New York City on April 21, 1929. He attended the Bronx High School of Science, received an A.B. in Mathematics from Harvard University in 1949, an M.A. in Mathematics from the University of California at Berkeley in 1951 and his Ph.D. in Mathematics and Statistics in 1953, also at Berkeley. He was a research scientist at Hudson Labs, Physics Department of Columbia University, from1953 to1956 and an Assistant Professor of Mathematical Statistics at Columbia from1955 to1956. In1956, he moved to Stanford University, where he stayed until1970, at which time he joined the faculty at the State University of New York at Buffalo, where he served first as Leading Professor and Chairman of the Department of Statistics and then as Director of Statistical Science. In1978 he moved to Texas A&M University as a Distinguished Professor, a post he currently holds. He has been a Fellow at Imperial College London, at IBM Systems Research Institute and at the Center for Advanced Study in the Behavioral Sciences at Stanford, as well as a Visiting Professor at the Sloan School of MIT, the Department of Statistics at Harvard and the Department of Biostatistics at Harvard. In 1959 he married Carol Tenowitz. They have two children and four grandchildren.

Professor Parzen has authored or coauthored over 100 papers and 6 books. He has served on innumerable editorial boards and national committees, and has organized several influential conferences and workshops. He has directed the research of many graduate students and provided advice, encouragement and collaboration to students and colleagues around the world. To honor these contributions, he has been elected a Fellow of the American Statistical Association, of the Institute of Mathematical Statistics and of the American Association for the Advancement of Science. In 1994, he was awarded the prestigious Samuel S. Wilks Memorial Medal by the American Statistical Association.

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Statist. Sci. Volume 17, Issue 3 (2002), 357-378.

First available in Project Euclid: 16 January 2003

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Newton, H. Joseph. A Conversation with Emanuel Parzen. Statist. Sci. 17 (2002), no. 3, 357--378. doi:10.1214/ss/1042727944.

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  • AKAIKE, H. (1974). A new look at the statistical model identification. IEEE Trans. Automatic Control AC-19 716-723.
  • ARONSZAJN, N. (1950). Theory of reproducing kernels. Trans. Amer. Math. Soc. 68 337-404.
  • FINDLEY, D. F. and PARZEN, E. (1995). A conversation with Hirotugu Akaike. Statist. Sci. 10 104-117.
  • GRENANDER, U. (1950). Stochastic processes and statistical inference. Ark. Mat. 1 195-277.
  • HIRSCHFIELD, H. O. (1935). A connection between correlation and contingency. Proc. Cambridge Philos. Soc. 31 520.
  • JENKINS, G. M. (1961). General considerations in the analysis of spectra. Technometrics 3 133-166.
  • LANNING, J. H. and BATTIN, R. H. (1956). Random Processes in Automatic Control. McGraw-Hill, New York.
  • MAKRIDAKIS, E., NEWTON, H. J., PARZEN, E. et al. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. J. Forecasting 1 111-153.
  • NEWTON, H. J. (1988). TIMESLAB: A Time Series Analy sis Laboratory. Wadsworth, Pacific Grove, CA.
  • OGDEN, T. and PARZEN, E. (1996). Data dependent wavelet thresholding in nonparametric regression with change point applications. Comput. Statist. Data Anal. 22 53-70.
  • PARZEN, E. (1958). On asy mptotically efficient consistent estimates of the spectral density function of a stationary time series. J. Roy. Statist. Soc. Ser. B 20 303-322.
  • PARZEN, E. (1961a). An approach to time series analysis. Ann. Math. Statist. 32 951-989.
  • PARZEN, E. (1961b). Mathematical considerations in the estimation of spectra. Technometrics 3 167-190.
  • PARZEN, E. (1962). On estimation of a probability density function and mode. Ann. Math. Statist. 33 1065-1076.
  • PARZEN, E. (1969). Multiple time series modeling. In Multivariate Analy sis-II (P. Krishnaiah, ed.) 389-409. Academic Press, New York.
  • PARZEN, E. (1974). Some recent advances in time series modeling. IEEE Trans. Automatic Control AC-19 723-730.
  • PARZEN, E. (1977). Multiple time series: Determining the order of approximating autoregressive schemes. In Multivariate Analy sis-IV (P. Krishnaiah, ed.) 283-295. North-Holland, Amsterdam.
  • PARZEN, E. (1979). Nonparametric statistical data modeling (with discussion). J. Amer. Statist. Assoc. 74 105-131.
  • PARZEN, E. (1982). ARARMA models for time series analysis and forecasting. J. Forecasting 1 67-82.
  • PARZEN, E. (1992). Comparison change analysis. In Nonparametric Statistics and Related Topics (A. K. Saleh, ed.) 3-15. North-Holland, Amsterdam.
  • PARZEN, E. (1992). Time series, statistics and information. In New Directions in Time Series Analy sis (D. Brillinger, P. Caines, J. Geweke, E. Parzen, M. Rosenblatt and M. S. Taqqu, eds.) 265-286. Springer, New York.
  • RAMSAY, J. O. and SILVERMAN, B. W. (1997). Functional Data Analy sis. Springer, New York.
  • SACKS, J. and YLVISAKER, D. (1968). Designs for regression problems with correlated errors; many parameters. Ann. Math. Statist. 39 40-69.
  • TUKEY, J. W. (1961). Discussion, emphasizing the connection between analysis of variance and spectrum analysis. Technometrics 3 191-219.