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- W873080561 abstract "We observe a group of pigs over a period of about 100 days. Using high frequency radio frequency identification, it is recorded when each pig is feeding, leading to very dense binary functional data for each pig and day. One aim of the data analysis is to find pig-specific feeding profiles showing us the typical feeding pattern of each pig. For modeling the data, we use a marginal functional logistic regression approach, allowing us to model the densely observed binary measurements by assuming an underlying smooth subject-specific profile. The method also allows to incorporate additional covariates such as temperature and humidity that may influence the pigs’ behavior. To account for correlation of measurements, we use robust standard errors and corresponding pointwise confidence intervals. Before analyzing the feeding behavior of pigs, the method employed is evaluated in simulation studies. As our approach is rather general, it may also be applied to other types of generalized functional data with similar characteristics as the pig data." @default.
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- W873080561 date "2015-07-07" @default.
- W873080561 modified "2023-10-02" @default.
- W873080561 title "Marginal Functional Regression Models for Analyzing the Feeding Behavior of Pigs" @default.
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- W873080561 doi "https://doi.org/10.1007/s13253-015-0212-7" @default.
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