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- W3201715906 abstract "Estimating the first-order intensity function in point pattern analysis is an important problem, and it has been approached so far from different perspectives: parametrically, semiparametrically or nonparametrically. Our approach is close to a semiparametric one. Motivated by eye-movement data, we introduce a convolution type model where the log-intensity is modeled as the convolution of a function β ( ⋅ ) , to be estimated, and a single spatial covariate (the image an individual is looking at for eye-movement data). Based on a Fourier series expansion, we show that the proposed model can be viewed as a log-linear model with an infinite number of coefficients, which correspond to the spectral decomposition of β ( ⋅ ) . After truncation, we estimate these coefficients through a penalized Poisson likelihood. We illustrate the efficiency of the proposed methodology on simulated data and on eye-movement data." @default.
- W3201715906 created "2021-10-11" @default.
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- W3201715906 date "2022-10-01" @default.
- W3201715906 modified "2023-10-16" @default.
- W3201715906 title "A convolution type model for the intensity of spatial point processes applied to eye-movement data" @default.
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- W3201715906 doi "https://doi.org/10.1016/j.spasta.2022.100651" @default.
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