What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Machine learning
Byron J. T. Morgan focuses on Statistics, Econometrics, Demography, Markov chain Monte Carlo and Bayesian probability.
His Statistics study frequently intersects with other fields, such as Heron.
Byron J. T. Morgan focuses mostly in the field of Econometrics, narrowing it down to topics relating to Mark and recapture and, in certain cases, Logistic regression.
The various areas that Byron J. T. Morgan examines in his Demography study include Population density, Ecology, Reproduction, Soay sheep and Survival analysis.
Byron J. T. Morgan studied Population density and Population size that intersect with Climate change, Population decline, Archipelago, Population model and Ovis.
His Markov chain Monte Carlo research is multidisciplinary, relying on both Covariate and Bayesian inference.
His most cited work include:
- Age, Sex, Density, Winter Weather, and Population Crashes in Soay Sheep (752 citations)
- Integrating Mark–Recapture–Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters (312 citations)
- Analysis of Quantal Response Data (256 citations)
What are the main themes of his work throughout his whole career to date?
Byron J. T. Morgan mainly focuses on Statistics, Econometrics, Mark and recapture, Ecology and Applied mathematics.
His study ties his expertise on Abundance together with the subject of Statistics.
His Econometrics research focuses on GLIM and how it connects with Overdispersion.
His Ecology study combines topics from a wide range of disciplines, such as Demography, Population size and Population model.
His biological study spans a wide range of topics, including Simple and Mathematical optimization.
Many of his research projects under Bayesian probability are closely connected to Random effects model with Random effects model, tying the diverse disciplines of science together.
He most often published in these fields:
- Statistics (40.17%)
- Econometrics (22.59%)
- Mark and recapture (9.62%)
What were the highlights of his more recent work (between 2009-2021)?
- Statistics (40.17%)
- Econometrics (22.59%)
- Ecology (9.21%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Statistics, Econometrics, Ecology, Abundance and Mark and recapture.
Estimator, Mixture model, Contrast, Score test and Data analysis are among the areas of Statistics where the researcher is concentrating his efforts.
His work on Covariate as part of general Econometrics study is frequently connected to Context, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His study in Population model extends to Ecology with its themes.
The Mark and recapture study combines topics in areas such as Demography, Data mining and Model selection.
In general Bayesian probability study, his work on Markov chain Monte Carlo often relates to the realm of Estimation, thereby connecting several areas of interest.
Between 2009 and 2021, his most popular works were:
- Design of occupancy studies with imperfect detection (135 citations)
- Analysis of Capture-Recapture Data (63 citations)
- Species Occupancy Modeling for Detection Data Collected Along a Transect (55 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Machine learning
His scientific interests lie mostly in Statistics, Ecology, Econometrics, Mark and recapture and Estimator.
Byron J. T. Morgan has researched Statistics in several fields, including Abundance and Computation.
His work in the fields of Ecology, such as Occupancy, Relative species abundance, Biodiversity and Ecosystem, overlaps with other areas such as Productivity.
Byron J. T. Morgan integrates Econometrics with Context in his study.
His Mark and recapture study combines topics in areas such as Simple, Data set and Joint probability distribution.
His Estimator research is multidisciplinary, incorporating elements of Sampling, Transect, Data mining and Cluster analysis.
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.
