What is he best known for?
The fields of study he is best known for:
- Statistics
- Quantum mechanics
- Algebra
His scientific interests lie mostly in Estimator, Econometrics, Applied mathematics, Mathematical optimization and Statistics.
His research in the fields of Survival function overlaps with other disciplines such as Gaussian process.
Richard D. Gill has included themes like Martingale, Statistical hypothesis testing, Missing data, Joint probability distribution and Survival data in his Econometrics study.
His Martingale research incorporates elements of Multivariate statistics and Regression dilution.
His study in Proportional hazards model, Covariate, Regression analysis and Counting process is carried out as part of his studies in Statistics.
The various areas that he examines in his Counting process study include Nelson–Aalen estimator, Data mining and Left truncation.
His most cited work include:
- Cox's Regression Model for Counting Processes: A Large Sample Study (3117 citations)
- Statistical Models Based on Counting Processes. (2513 citations)
- Censoring and stochastic integrals (482 citations)
What are the main themes of his work throughout his whole career to date?
Richard D. Gill mostly deals with Statistics, Statistical physics, Applied mathematics, Mathematical economics and Econometrics.
His research related to Expectation–maximization algorithm and Proportional hazards model might be considered part of Statistics.
His work carried out in the field of Statistical physics brings together such families of science as Quantum system, Quantum entanglement, State, Quantum information and Quantum statistical mechanics.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Estimator, Pointwise, Mathematical optimization and Conditional probability distribution.
Richard D. Gill has researched Mathematical economics in several fields, including Argument and Bell's theorem.
His study in Econometrics is interdisciplinary in nature, drawing from both Regression analysis, Markov process, Survival data and Statistical hypothesis testing.
He most often published in these fields:
- Statistics (15.62%)
- Statistical physics (12.89%)
- Applied mathematics (12.89%)
What were the highlights of his more recent work (between 2011-2021)?
- Bell's theorem (7.81%)
- Hidden variable theory (7.03%)
- Simple (6.25%)
In recent papers he was focusing on the following fields of study:
Richard D. Gill spends much of his time researching Bell's theorem, Hidden variable theory, Simple, Quantum and Mathematical economics.
His Bell's theorem research integrates issues from Quantum nonlocality and Counterexample.
In his study, which falls under the umbrella issue of Quantum, Statistical physics is strongly linked to Trigonometric functions.
His Mathematical economics study incorporates themes from Judgement, Argument, Contradiction and Bayesian inference.
His work investigates the relationship between Causality and topics such as No-communication theorem that intersect with problems in Statistics.
His work blends Statistics and Forensic statistics studies together.
Between 2011 and 2021, his most popular works were:
- On asymptotic quantum statistical inference (49 citations)
- Statistics, causality and Bell's theorem (43 citations)
- Quantum local asymptotic normality based on a new quantum likelihood ratio (42 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Quantum mechanics
- Mathematical analysis
Richard D. Gill mainly investigates Bell's theorem, Applied mathematics, Quantum, Mathematical economics and Normalization.
His Bell's theorem study combines topics from a wide range of disciplines, such as Causality, Quantum nonlocality and Statistical physics.
Richard D. Gill has researched Applied mathematics in several fields, including Estimator, Strong consistency, Bounded function and Probability mass function.
The Quantum study which covers Local asymptotic normality that intersects with Domain, Statistical model, Smoothness, State and Statistical inference.
His research in Normalization tackles topics such as Discrete mathematics which are related to areas like Calculus.
His Calculus research is multidisciplinary, relying on both No-communication theorem, Mistake, Algebraic error and Statistics.
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