2022 - Research.com Mathematics in United Kingdom Leader Award
2001 - Fellow of the American Statistical Association (ASA)
Peter J. Diggle mostly deals with Statistics, Point process, Data mining, Applied mathematics and Spatial variability. His work on Econometrics expands to the thematically related Statistics. His Econometrics research is multidisciplinary, relying on both Logistic regression, Missing data, Marginal model, Covariance and Dropout.
His Point process research is multidisciplinary, incorporating perspectives in Point, Statistical physics, Inference and Cox process. His research in Data mining intersects with topics in Point pattern analysis, Linear model and Set. The study incorporates disciplines such as Smoothing, Kriging, Estimator, Bivariate analysis and Series in addition to Applied mathematics.
The scientist’s investigation covers issues in Statistics, Econometrics, Point process, Environmental health and Data mining. His research in Sampling, Poisson distribution, Spatial variability, Nonparametric statistics and Monte Carlo method are components of Statistics. His work on Environmental health is being expanded to include thematically relevant topics such as Public health.
His research is interdisciplinary, bridging the disciplines of Epidemiology and Public health. Much of his study explores Data mining relationship to Inference.
Peter J. Diggle spends much of his time researching Demography, Environmental health, Severe acute respiratory syndrome coronavirus 2, Public health and Coronavirus disease 2019. The Demography study combines topics in areas such as Health care, Odds, Seasonality, Vaccination and Confounding. Peter J. Diggle interconnects Psychological intervention, Malaria and Incidence in the investigation of issues within Environmental health.
Many of his studies on Severe acute respiratory syndrome coronavirus 2 apply to Pandemic as well. His Public health research integrates issues from Leptospirosis, Leptospira and Epidemiology. Peter J. Diggle combines subjects such as Confidence interval and 2019-20 coronavirus outbreak with his study of Coronavirus disease 2019.
His primary scientific interests are in Demography, Severe acute respiratory syndrome coronavirus 2, Coronavirus disease 2019, Interim report and Public health. His studies in Demography integrate themes in fields like Proportional hazards model, Odds, Health care and Propensity score matching. He focuses mostly in the field of Severe acute respiratory syndrome coronavirus 2, narrowing it down to topics relating to Pandemic and, in certain cases, Population level, 2019-20 coronavirus outbreak and Onchocerciasis.
His Public health study combines topics from a wide range of disciplines, such as Epidemiology, Environmental epidemiology, Environmental health, Statistical inference and Range. His research integrates issues of Mental health, Malaria, Incidence and Health policy in his study of Environmental health. His Incidence research is multidisciplinary, incorporating elements of Veterinary medicine, Data collection, Sewage and Spatial variability.
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Analysis of longitudinal data
Peter Diggle;Patrick J. Heagerty;Kung-Yee Liang;Scott L. Zeger.
Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
Peter J. Diggle.
Statistical analysis of spatial point patterns
Peter J. Diggle.
P. J. Diggle;J. A. Tawn;R. A. Moyeed.
Journal of The Royal Statistical Society Series C-applied Statistics (2002)
Informative Drop‐Out in Longitudinal Data Analysis
P. Diggle;M. G. Kenward.
Applied statistics (1994)
Time Series: A Biostatistical Introduction
Peter J Diggle.
Joint modelling of longitudinal measurements and event time data.
Robin Henderson;Peter J. Diggle;Angela Dobson.
Handbook of spatial statistics
Alan E. Gelfand;Peter Diggle;Peter Guttorp;Montserrat Fuentes.
Spatial point pattern analysis and its application in geographical epidemiology
Anthony C. Gatrell;Trevor C. Bailey;Peter J. Diggle;Barry S. Rowlingson.
Transactions of the Institute of British Geographers (1996)
A Kernel Method for Smoothing Point Process Data
Journal of The Royal Statistical Society Series C-applied Statistics (1985)
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