Asymmetric interactions between different species of molecules have previously been demonstrated to result in self-organized ...

Generalized linear models (GLMs) are widely used for data analysis; however, their likelihood estimators can be sensitive to outliers. We propose new statistical models that allow robust inferences ...

In this paper, we propose a functional linear regression model in the space of probability density functions. We treat a cross-sectional distribution of individual earnings as an infinite dimensional ...

Relative risk (RR) of acute respiratory infections by particulate matter (PM 2.5), using the distributed lag non-linear model with a spatial function to analyze the data imputed by the local time ...

We consider a generalized partially linear model E (Y|X, T) = G {X T β + m (T)}, where G is a known function, β is an unknown parameter vector, and m is an unknown function. We introduce a test ...

However, AI models are often used to find intricate patterns in data where the output is not always proportional to the input. For this, you also need non-linear thresholding functions that adjust ...

Deforming them is tricky and the deformations must be integrated into the actual model. That is, without something like a non-linear deformation space, as this paper talks about.

Catalog : MATH.5910 Linear Statistics Modeling and Regression (Formerly 92.591) Id: 008463 Credits Min: 3 Credits Max: 3 Description Model building via linear regression models. Method of least ...