A frequent object of study in linguistic typology is the order of elements {demonstrative, adjective, numeral, noun} in the noun phrase. The goal is to predict the relative frequencies of these orders across languages. Here we use Poisson regression to statistically compare some prominent accounts of this variation. We compare feature systems derived from Cinque (2005) to feature systems given ...

We show that each triangular Poisson Lie group can be decomposed into Poisson submanifolds each of which is a quotient of a symplectic manifold. The Marsden–Weinstein–Meyer symplectic reduction technique is then used to give a complete description of the symplectic foliation of all triangular Poisson structures on Lie groups. The results are illustrated in detail for the generalized Jordanian P...

The likelihood ratio spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection applications. In order to better understand cluster mechanisms, an equivalent model-based approach is proposed to the spatial scan statistic that unifies currently loosely coupled methods for including ecological covariates in the spatial scan test. In addition, the uti...

Regression models are one of the most important used in modern studies, especially research and health studies because results they achieve. Two regression were used: Poisson Model Conway-Max Well- Poisson), where this study aimed to make a comparison between two choose best them using simulation method at different sample sizes (n = 25,50,100) with repetitions (r 1000). The Matlab program was ...

Adaptive choice of smoothing parameters for nonparametric Poisson regression (O’Sullivan et. al., 1986) is considered in this paper. A computable approximation of the unbiased risk estimate (AUBR) for Poisson regression is introduced. This approximation can be used to automatically tune the smoothing parameter for the penalized likelihood estimator. An alternative choice is the generalized appr...

This paper develops a censored generalized Poisson regression model that can be used to predict a response variable that is a2ected by one or more explanatory variables. The censored generalized Poisson regression model is suitable for modeling count data that exhibit either overor under-dispersion. The regression parameters are estimated by the method of maximum likelihood and approximate test...

In this paper we present a review of some results about inference based on φ-divergence measures, under assumptions of multinomial sampling and loglinear models. The minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator is considered. This estimator is used in a φdivergence measure which is the basis of new statistics for solving three importan...

We consider L1-isotonic regression and L∞ isotonic and unimodal regression. For L1isotonic regression, we present a linear time algorithm when the number of outputs are bounded. We extend the algorithm to construct an approximate isotonic regression in linear time when the output range is bounded. We present linear time algorithms for L∞ isotonic and unimodal regression.