1960 - Fellow of the American Statistical Association (ASA)
James Durbin mainly focuses on Statistics, Series, Regression, State space and Time series. His biological study spans a wide range of topics, including Distribution and Applied mathematics. In his research, Data mining and Algorithm is intimately related to Range, which falls under the overarching field of Series.
His studies in State space integrate themes in fields like Exponential family, Heavy-tailed distribution, Estimation theory, Exponential distribution and Monte Carlo method. His Time series research is multidisciplinary, relying on both Durbin–Watson statistic, Econometrics, Polynomial regression, Independence and Breusch–Godfrey test. His Regression analysis research includes themes of CUSUM, Statistic, Stability and Constant.
The scientist’s investigation covers issues in Statistics, Applied mathematics, State space, Series and Smoothing. Regression, Regression analysis, Autocorrelation, Statistical hypothesis testing and Linear regression are the primary areas of interest in his Statistics study. As part of one scientific family, James Durbin deals mainly with the area of Regression analysis, narrowing it down to issues related to the Econometrics, and often Seasonal adjustment.
The concepts of his State space study are interwoven with issues in State vector, Data mining, Importance sampling and Time series. He works mostly in the field of Time series, limiting it down to topics relating to Feature and, in certain cases, Range and Development, as a part of the same area of interest. In his study, which falls under the umbrella issue of Smoothing, Mathematical proof, Multivariate statistics and Kalman filter is strongly linked to Algorithm.
His primary areas of study are Smoothing, Applied mathematics, State space, Time series and Nonlinear system. He has included themes like Importance sampling and Pattern recognition in his Smoothing study. His Importance sampling study is related to the wider topic of Statistics.
His study in the fields of Generalized linear mixed model under the domain of Applied mathematics overlaps with other disciplines such as State space. As a member of one scientific family, James Durbin mostly works in the field of State space, focusing on Series and, on occasion, Linear regression and Measure. His work carried out in the field of Algorithm brings together such families of science as Range, Development and Regression.
His main research concerns State space, Time series, Calculus, Operations research and Applied mathematics. His State space research includes elements of Autoregressive integrated moving average, Series, Regression, Range and Algorithm. He interconnects Development and Feature in the investigation of issues within Time series.
His work on Generalized linear mixed model as part of his general Applied mathematics study is frequently connected to State space, thereby bridging the divide between different branches of science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Techniques for Testing the Constancy of Regression Relationships Over Time
Robert L. Brown;J. Durbin;J. M. Evans.
Journal of the royal statistical society series b-methodological (1975)
Testing for serial correlation in least squares regression. II.
J. Durbin;G. S. Watson.
Time Series analysis by state space methods
James Durbin;Siem Jan Koopman.
ACM Transactions on Spatial Algorithms and Systems (2012)
Time Series Analysis by State Space Methods
James Durbin;Siem Jan Koopman.
Research Papers in Economics (2001)
The fitting of time series models
Revue de l'Institut International de Statistique / Review of the International Statistical Institute , 28 (3) pp. 233-244. (1960) (1960)
A Simple and Efficient Simulation Smoother for State Space Time Series Analysis
J. Durbin;S. J. Koopman.
Monte Carlo maximum likelihood estimation for non-Gaussian state space models
J. Durbin;S.J.M. Koopman.
HIV testing on all pregnant women.
Douglas Black;Walter Bodmer;David Cox;Richard Doll.
The Lancet (1987)
Distribution theory for tests based on the sample distribution function
Estimation of Parameters in Time-Series Regression Models
Journal of the royal statistical society series b-methodological (1960)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: