What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Normal distribution
Richard L. Tweedie focuses on Markov chain, Ergodicity, Ergodic theory, Bounded function and Applied mathematics.
His Ergodicity study combines topics in areas such as Central limit theorem and Pure mathematics.
His Ergodic theory study integrates concerns from other disciplines, such as Space, Topological space and Combinatorics.
His Applied mathematics study combines topics from a wide range of disciplines, such as Markov property, Discrete phase-type distribution, Markov process and Exponential function.
His research in Markov model focuses on subjects like Mathematical proof, which are connected to Tweedie distribution.
His Tweedie distribution research incorporates elements of Range, Mathematical economics and Operations research.
His most cited work include:
- Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. (6734 citations)
- Markov Chains and Stochastic Stability (4450 citations)
- A Nonparametric “Trim and Fill” Method of Accounting for Publication Bias in Meta-Analysis (1714 citations)
What are the main themes of his work throughout his whole career to date?
Richard L. Tweedie mainly focuses on Markov chain, Applied mathematics, Ergodicity, Markov process and Combinatorics.
In Markov chain, Richard L. Tweedie works on issues like Discrete mathematics, which are connected to Space, Exponential ergodicity, Coupling and State.
His Applied mathematics research incorporates themes from SETAR, STAR model, Econometrics, Simple and Exponential function.
His studies in Econometrics integrate themes in fields like Meta-analysis, Publication bias, Funnel plot and Random effects model.
The study incorporates disciplines such as Ergodic theory, Pure mathematics, Invariant measure, Bounded function and Random walk in addition to Ergodicity.
The various areas that Richard L. Tweedie examines in his Markov process study include Tweedie distribution and Stationary distribution.
He most often published in these fields:
- Markov chain (46.43%)
- Applied mathematics (26.79%)
- Ergodicity (19.64%)
What were the highlights of his more recent work (between 1999-2009)?
- Markov chain (46.43%)
- Applied mathematics (26.79%)
- Markov model (11.61%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Markov chain, Applied mathematics, Markov model, Examples of Markov chains and Markov property.
His primary area of study in Markov chain is in the field of Coupling from the past.
His work deals with themes such as Rate of convergence, SETAR and Econometrics, which intersect with Applied mathematics.
His Econometrics research includes themes of Epistemology, Causation, Outcome and Rank.
His Markov model study is concerned with the field of Markov process as a whole.
His Markov renewal process study combines topics from a wide range of disciplines, such as Markov kernel and Markov chain mixing time.
Between 1999 and 2009, his most popular works were:
- Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. (6734 citations)
- A Nonparametric “Trim and Fill” Method of Accounting for Publication Bias in Meta-Analysis (1714 citations)
- Non-Gaussian conditional linear AR(1) models (107 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Normal distribution
His primary areas of investigation include Meta-analysis, Markov chain, Econometrics, Applied mathematics and Monotone polygon.
His Meta-analysis study which covers Statistics that intersects with Rank.
His Markov chain study integrates concerns from other disciplines, such as Markov process and Combinatorics.
His research in Econometrics intersects with topics in Autoregressive integrated moving average, SETAR, Stationary process and Model selection.
His research integrates issues of STAR model and Autoregressive model in his study of Applied mathematics.
His Publication bias research is multidisciplinary, incorporating elements of Missing data, Estimator, Selection bias, Point estimation and Random effects model.
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.
