Exploratory analysis of association models using loglinear models and singular value decompositions

Learning Verbal Transitivity Using LogLinear Models

Portuguese dictionaries lack information about the kind of phrases or clauses a verb goes with. In this paper we describe how this information can be computationally learned from an automatically tagged corpus with almost 10,000,000 words. Loglinear modeling for categorical data will be used to analyze and automatically identify the subcatego-rization dependencies. Loglinear models were also us...

Continuation of Singular Value Decompositions

In this work we consider computing a smooth path for a (block) singular value decomposition of a full rank matrix valued function. We give new theoretical results and then introduce and implement several algorithms to compute a smooth path. We illustrate performance of the algorithms with a few numerical examples.

Analysis of Birth Spacing Using Frailty Models

Background and objectives: Birth spacing is an important variable for identification of fertility acceleration, total fertility rate, and maternal and fetal health. Therefore, special attention has been paid to this issue by researchers in the fields of medical sciences, health, and population. In addition, proper analysis of this concept is of foremost importance. Application of classical anal...

Negative Binomial Loglinear Mixed Models

Eigenvalues and Singular Values 10.1 Eigenvalue and Singular Value Decompositions

This chapter is about eigenvalues and singular values of matrices. Computational algorithms and sensitivity to perturbations are both discussed. An eigenvalue and eigenvector of a square matrix A are a scalar λ and a nonzero vector x so that Ax = λx. A singular value and pair of singular vectors of a square or rectangular matrix A are a nonnegative scalar σ and two nonzero vectors u and v so th...