What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Random variable
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.
His most cited work include:
- Testing for serial correlation in least squares regression. II. (2956 citations)
- Techniques for Testing the Constancy of Regression Relationships Over Time (2797 citations)
- Time Series Analysis by State Space Methods (1090 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Statistics (28.75%)
- Applied mathematics (25.00%)
- State space (22.50%)
What were the highlights of his more recent work (between 2004-2012)?
- Smoothing (15.00%)
- Applied mathematics (25.00%)
- State space (22.50%)
In recent papers he was focusing on the following fields of study:
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.
Between 2004 and 2012, his most popular works were:
- Time Series analysis by state space methods (1005 citations)
- Time Series Analysis by State Space Methods: Second Edition (82 citations)
- Linear state space models (6 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Random variable
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.
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