Howell Tong

rdf:langString Howell Tong

rdf:langString Howell Tong

rdf:langString Howell Tong

rdf:langString Chinese University of Hong Kong

rdf:langString London School of Economics

rdf:langString University of Hong Kong

rdf:langString University of Kent

rdf:langString University of Manchester Institute of Science and Technology

rdf:langString University of Electronic Science and Technology of China, China

rdf:langString Tsinghua University, China

rdf:langString Academy of Mathematics and System Sciences, the Chinese Academy of Sciences

rdf:langString Honorary Fellow of the Institute and Faulty of Actuaries, U.K.

rdf:langString State Prize Class 2 for Natural Sciences, China

rdf:langString Distinguished Achievement Award, International Chinese Statistical Association

rdf:langString Guy Medal in Silver, Royal Statistical Society, U.K.

rdf:langString Hongzhi An; Kung-Sik Chan; Rong Chen; Bing Cheng; Jan DeGooijer; Cees Diks; Barbel Finkenstaedt; Simone Giannerini; Dong Li; Wai Keung Li; Shiqing Ling; Manfred Mudelsee; Ken Siu; Ruey Tsay; Yingcun Xia; Qiwei Yao

rdf:langString 湯家豪

rdf:langString Tāng Jiāháo

rdf:langString 汤家豪

rdf:langString 湯家豪

rdf:langString 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 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 thecontinuing freshness, boldness and spirit of enquiry which inform them-indeed, proper qualities foran explorer. He stands as the recognised innovator and authority in his subject, while remainingdisarmingly direct and enthusiastic .’¹ And 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.

rdf:langString 湯家豪