Howell Tong

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Howell Tong
湯家豪
Born	1944 (age 79–80) Hong Kong
Alma mater	University of Manchester Institute of Science and Technology
Known for	Threshold models; nonlinear time series; nonstationary time series; chaos; dimension reduction; model selection; likelihood-free statistics; Markov chain modeling; reliability
Awards	Foreign Member of Norwegian Academy of Science and Letters Guy Medal in Silver, Royal Statistical Society, U.K. State Prize Class 2 for Natural Sciences, China Honorary Fellow of the Institute and Faulty of Actuaries, U.K. Distinguished Achievement Award, International Chinese Statistical Association
Scientific career
Fields	Statistics
Institutions	Northern Polytechnic London (1967-68) University of Manchester Institute of Science and Technology (1968–82) Chinese University of Hong Kong (1982–85) University of Kent (1986–99) University of Hong Kong (1997–2004) London School of Economics (1999–2009) Academy of Mathematics and System Sciences, the Chinese Academy of Sciences (2000-2004) University of Electronic Science and Technology of China, China (2016–2021) Tsinghua University, China (2019-now)

Howell Tong (simplified Chinese: 汤家豪; traditional Chinese: 湯家豪; pinyin: Tāng Jiāháo; born in 1944 in Hong Kong) is a statistician who has made fundamental contributions to nonlinear time series analysis, semi-parametric statistics, non-parametric statistics, dimension reduction, model selection, likelihood-free statistics and other areas. In the words of Professor Peter Whittle (FRS): "The striking feature of Howell Tong's … is the continuing freshness, boldness and spirit of enquiry which inform them-indeed, proper qualities for an explorer. He stands as the recognised innovator and authority in his subject, while remaining disarmingly direct and enthusiastic."^[1] His work, in the words of Sir David Cox, "links two fascinating fields, nonlinear time series and deterministic dynamical systems."^[2] He is the father of the threshold time series models, which have extensive applications in ecology, economics, epidemiology and finance. (See external links for detail.) Besides nonlinear time series analysis, he was the co-author of a seminal paper, which he read to the Royal Statistical Society, on dimension reduction in semi-parametric statistics by pioneering the approach based on minimum average variance estimation. He has also made numerous novel contributions to nonparametric statistics (obtaining the surprising result that cross-validation does not suffer from the curse of dimensionality for consistent estimation of the embedding dimension of a dynamical system), Markov chain modelling (with application to weather data), reliability, non-stationary time series analysis (in both the frequency domain and the time domain) and wavelets.