After model selection, subsequent inference in statistical models tends to be overconfident if selection is not accounted for. One possible solution address this problem selective inference, which constitutes a post-selection framework and yields valid statements by conditioning on the event. Existing work is, however, directly applicable additive linear mixed models. A novel extension recent c...

a number of 5558 lambing records of 2245 mehraban ewes, collected during years 1995 to 2010 were utilized to estimate the variance components and heritability for the number of lambs per lambing,  making use of b-spline random regression models. the fixed factors affecting the studied trait were identified using general linear model analysis as well as sas software, where flock-year-season and ...

Generalized additive models (GAMs) have been widely used. While the procedure for fitting a generalized additive model to independent data has been well established, not as much work has been done when the data are correlated. The currently available methods are not completely satisfactory in practice. A new approach is proposed to fit generalized additive models with spatio-temporal data via t...

Additive clustering provides a conceptually simple and potentially powerful approach to modeling the similarity relationships between stimuli. The ability of additive clustering models to accommodate similarity data, however, typically arises through the incorporation of large numbers of parameterized clusters. Accordingly, for the purposes of both model generation and model comparison, it is n...

We propose new models to segment images corrupted by additive or multiplicative noise, in a variational level set approach. In the additive case, we decompose the data into the sum . Here, is a piecewise-constant component, capturing edges and discontinuities, and it is modeled in a level set approach, while is a smooth component, capturing the intensity inhomogeneities. The additive noise is r...