We propose a multiplicative speci cation of a discrete choice model that renders choice probabilities independent of the scale of the utility. The scale can thus be random with unspeci ed distribution. The model mostly outperforms the classical additive formulation over a range of stated choice data sets. In some cases, the improvement in likelihood is greater than that obtained from adding obs...

The Kernel based Nonlinear Subspace (KNS) method is proposed for multi-class pattern classi cation. This method consists of the nonlinear transformation of feature spaces de ned by kernel functions and subspace method in transformed high-dimensional spaces. The Support Vector Machine, a nonlinear classi er based on a kernel function technique, shows excellent classi cation performance, however,...

Allocation processes|the division of some commodity among multiple agents|are fundamental to social interactions in various arenas. Examples include wealth/income distribution in populations, natural resource exploitation, market share for competing corporations, satellite bandwidth division among many users, and CPU time usage by multiple software agents running simultaneously. In the case whe...

This paper presents new identi…cation results for the class of structural dynamic discrete choice models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where utility function of one choice in the model is parametric but the dis...

Cation transporters/channels are key players in a wide range of physiological functions in plants, including cell signaling, osmoregulation, plant nutrition and metal tolerance. The recent identification of genes encoding some of these transport systems has allowed new studies toward further understanding of their integrated roles in plant. This review summarizes recent discoveries regarding th...

System identi®cation involves determination of the functional structure of a target system that underlies the observed data. In this paper, we present a probabilistic evolutionary method that optimizes system architectures for the iden-ti®cation of unknown target systems. The method is distinguished from existing evolutionary algorithms (EAs) in that the individuals are generated from a probabi...

This work studies implicitly weighted robust statistical methods suitable for econometric problems. We study robust estimation mainly for the context of heteroscedasticity or high dimension, which are up-to-date topics of current econometrics. We describe a modifi cation of linear regression resistant to heteroscedasticity and study its computational aspects. For a robust version of the instrum...

This paper considers the construction of median unbiased forecasts for near-integrated autoregressive processes. It derives the appropriately scaled limiting distribution of the deviation of the forecast from the true conditional mean. The dependence of the limiting distribution on nuisance parameters precludes the use of the standard asymptotic and bootstrap methods for bias correction. We pro...