Profile
Peter Craigmile’s research interests include time series and longitudinal analysis, spatial statistics, and spatio-temporal modeling. In collaboration with others, he has developed methods for spatial exeedances and extremes which is critical to assessing spatially varying risk of environmental change or disease. Peter works on building scientifically relevant hierarchical statistical models, applied to areas such as climate science, public health, psychology, environmental health, neurophysiology, and medicine. He is comfortable in carrying out collaborative interdisciplinary research. Peter has experience in using data science and statistical learning techniques applied to different areas such as analyzing electroencephalogram (EEG) series via spectral and wavelet decompositions. He is currently interested in developing statistical methodologies for the statistical inference and theory surrounding stationary and nonstationary non-Gaussian spatial and temporal processes. Further areas of research include the development of statistical methodologies for assimilating computer experiment model output with data, as is commonplace in modeling climate change, disease mapping, and environmental risk assessment, for example.
Peter is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, The Royal Statistical Society, and an elected member of the International Statistical Institute. He is also a member of the International Environmetrics Society, and the International Society for Bayesian Analysis.