Howell Tong (; born in 1944 in Hong Kong) is a statistician who has made foundational contributions to nonlinear time series analysis, besides fundamental contributions to semi-parametric statistics, non-parametric statistics, dimension reduction, model selection, likelihood-free statistics, gradient-based entropy 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." His work, in the words of Sir David Cox (FRS), "links two fascinating fields, nonlinear time series and deterministic dynamical systems."
His Threshold Principle allows the analysis of a complex stochastic system by decomposing it into simpler subsystems. 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 another seminal paper, which became the fifth paper that he read to the Royal Statistical Society. This paper on dimension reduction in semi-parametric statistics pioneered 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), gradient-based entropy (covering both normalized and non-normalized probability densities, beyond the Shannon entropy), 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.
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Howell Tong (; born in 1944 in Hong Kong) is a statistician who has made foundational contributions to nonlinear time series analysis, besides fundamental contributions to semi-parametric statistics, non-parametric statistics, dimension reduction, model selection, likelihood-free statistics, gradient-based entropy 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." His work, in the words of Sir David Cox (FRS), "links two fascinating fields, nonlinear time series and deterministic dynamical systems."
His Threshold Principle allows the analysis of a complex stochastic system by decomposing it into simpler subsystems. 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 another seminal paper, which became the fifth paper that he read to the Royal Statistical Society. This paper on dimension reduction in semi-parametric statistics pioneered 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), gradient-based entropy (covering both normalized and non-normalized probability densities, beyond the Shannon entropy), 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.
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