Point process models for sweat gland activation observed with noise
| Autor: | Mari Myllymäki, Aila Särkkä, Adam Loavenbruck, Mikko Kuronen |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences 62F15 (Primary) 62N39 60G55 (Secondary) Epidemiology Computer science Inference Sweating Bayesian inference 01 natural sciences Statistics - Applications Point process SWEAT Methodology (stat.ME) 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Sweat gland medicine Image Processing Computer-Assisted Humans Applications (stat.AP) 0101 mathematics Statistics - Methodology integumentary system business.industry Healthy subjects Pattern recognition Sweat Glands medicine.anatomical_structure Noise (video) Artificial intelligence business Point process models 030217 neurology & neurosurgery |
| Zdroj: | Statistics in medicineReferences. 40(8) |
| ISSN: | 1097-0258 |
| Popis: | The aim of the paper is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors. 27 pages, 12 figures |
| Databáze: | OpenAIRE |
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