SUMMARY In biological assays, clinical trials, life-testing problems, reliability and survival analysis, and a variety of applied fields ranging from agrometry to biodivrersity to zodiacal sciences, generalized linear models covering usual linear, transformed linear, log-linear and nonlinear regression models, multi-nomial and logistic regression models, as well as some semiparametric ones, are...

The Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of log-linear-by-linear association (LLLA) models, which are derived from a Rasch family of models. The ...

Several authors have previously discussed the use of log-linear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual log-linear modeling techniques, however, do not allow time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We ...

In this paper, we first set out a method for describing the age and spatial structures of interregional migration in Italy over time using the parameters of a saturated log-linear model. Second, we use this information and the log-linear model to project these structures forward based on a set of assumptions about the overall, main effect, and interaction parameters. These two sections of the p...

Semiparametric estimation of the memory parameter in economic time series raises the problem of the small sample size and the poor approximation of the asymptotic distribution to the finite sample counterpart. This paper considers the bootstrap to improve the finite sample distribution of the popular log peridogram regression and shows that it can significantly reduce the error in the coverage ...

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival...

Appositions are adjacent NPs used to add information to a discourse. We propose systems exploiting syntactic and semantic constraints to extract appositions from OntoNotes. Our joint log-linear model outperforms the state-of-the-art Favre and Hakkani-Tür (2009) model by ∼10% on Broadcast News, and achieves 54.3% Fscore on multiple genres.