Blinder-Oaxaca Decomposition for Tobit Models In this paper, a decomposition method for Tobit-models is derived, which allows the differences in a censored outcome variable between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. The method is applied to a decomposition of t...

In this paper, use the trajectory decomposition technique to rigorously prove the similarity between different neural models. First, we derive the free energy function for an unsupervised net of stochastic neurons with lateral interactions. The temperature incorporated in this function serves as control parameter in the annealing schedule. Then, we consider the incremental and batch modes of le...

Probability Mcatrix Decompositioni models mtay bve uised to model observed binary associations between two sets of elements. More specifically, to explain observed associations betweeni two elements, it is assumed that B laitent Bernoulli variables are realized for each element and that these variables are subsequently mapped into an observed data point accordingg to a prespecijied dererministi...

The covariance between two variables in a multivariate Gaussian distribution is decomposed into a sum of path weights for all paths connecting the two variables in an undirected independence graph. These weights are useful in determining which variables are important in mediating correlation between the two path endpoints. The decomposition arises in undirected Gaussian graphical models and doe...

ABSTRACT: The storage capability of full waveform is the state-of-the-art technology of LIDAR. In addition to the 3D point information recorded by conventional LIDAR systems, waveform LIDAR systems encode the intensity of returned signal along the time domain. This provides a user the possibility to decompose the waveform for the detection of illuminated target points. Therefore, the locations ...

Analyses of array-valued datasets often involve reduced-rank array approximations, typically obtained via least-squares or truncations of array decompositions. However, least-squares approximations tend to be noisy in high-dimensional settings, and may not be appropriate for arrays that include discrete or ordinal measurements. This article develops methodology to obtain low-rank model-based re...

Partitioning Hilbert space into two subspaces by using orthogonal projection operators yields compact forms for effective Hamiltonians for each of the subspaces. When one (the Q space) contains molecular bound states and the other (the P space) contains dissociative continua, a simple form for the non-Hermitian Q-space effective Hamiltonian, H(eff), can be obtained, subject to reasonable approx...

This article is the first of a series of papers dealing with domain decomposition algorithms for implicit solvent models. We show that, in the framework of the COSMO model, with van der Waals molecular cavities and classical charge distributions, the electrostatic energy contribution to the solvation energy, usually computed by solving an integral equation on the whole surface of the molecular ...

Effective spatial domain decomposition for discrete ordinate (Sn) neutron transport calculations has been critical for exploiting massively parallel architectures typified by the ASCI White computer at Lawrence Livermore National Laboratory. A combination of geometrical and computational constraints has posed a unique challenge as problems have been scaled up to several thousand processors. Car...