This paper reports the development of loglinear models for the disambiguation in wide-coverage HPSG parsing. The estimation of log-linear models requires high computational cost, especially with widecoverage grammars. Using techniques to reduce the estimation cost, we trained the models using 20 sections of Penn Treebank. A series of experiments empirically evaluated the estimation techniques, ...

A random-walk Metropolis sampler is geometrically ergodic if its equilibrium density is super-exponentially light and satisfies a curvature condition (Jarner and Hansen, 2000). Many applications, including Bayesian analysis with conjugate priors of logistic and Poisson regression and of log-linear models for categorical data result in posterior distributions that are not super-exponentially lig...

OBJECTIVE To assess the suitability of either the log-linear or reciprocal-linear regression procedure for maternal weight correction of biochemical marker MoMs in the first trimester. METHODS Data from two prospective first-trimester OSCAR screening programmes including 32,010 women with first-trimester maternal serum-free beta-hCG and PAPP-A measured by the Kryptor analyser was analysed by ...

Biomass estimates are required for reporting carbon, assessing feedstock availability, and assessing forest fire threat. We developed diameterand height-based biomass equations for Western hemlock (Tsuga heterophylla (Raf.) Sarg.) and red alder (Alnus rubra Bong.) trees in Western Oregon. A system of component biomass equations was fitted simultaneously with a constrained seemingly unrelated re...

1 SUMMARY. This article proposes a semiparametric Bayesian approach for inference on an unknown isotonic regression function, f (x), characterizing the relationship between a continuous predictor, X, and a response variable, Y , adjusting for covariates, Z. A novel prior formulation is used, which avoids parametric assumptions on f (x), while enforcing the non-decreasing constraint and assignin...

This document contains supplementary material to the article “Maximum Likelihood Estimation in LogLinear Models” by S.E. Fienberg and A. Rinaldo, henceforth referred to as FR. In section 2 we provide the proofs to some of the results announced in the article. Throughout, we assume familiarity with basic notions of polyhedral geometry: see Ziegler (1998), Schrijver (1998) and Rockafellar (1970) ...