Clustering in bioinformatics is a fundamental process involving computational issues that are far from being resolved. In our work, we propose a new approach to this problem and show preliminary comparisons to current leading methods in the field.

The paper uses nonparametric methodology to examine the role of institutions in understanding differential levels of development across countries. This technique estimates first order derivatives for every country allowing a deeper look into the impact of institutions on development. The preliminary cross-country findings show that (i) institutional quality positively and significantly increase...

This study was designed to examine the effect of the fragile X premutation (pM) on cognitive function and behavior. Participants included 14 children (7 males, 7 females) with the fragile X pM and 14 children without the fragile X pM (and without the fragile X full mutation [fM]), each of whom was matched by age and gender with one of the participants from the pM group. The children ranged in a...

BACKGROUND Chile is in the advanced stage of demographic transition with a low natural growth and a progressively aging population. AIM To analyze births by range of maternal age and to establish if population aging is expressed in the women's fertile age range. MATERIAL AND METHODS Trend analysis of births and age-specific birth rates in the period 1990-2004, in the Chilean women populatio...

Developed by General Motors (GM), the Auto Choice Advisor web site (http://www.auto choiceadvisor.com) recommends vehicles to consumers based on their requirements and budget constraints. Through the web site, GM has access to large quantities of data that reflect consumer preferences. Motivated by the availability of such data, we formulate a non-parametric approach to multi-product pricing. W...

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean-variance model in which the variance is replaced with an industry standard risk measure, Value-atRisk (VaR), in order to better assess market risk exposure associated with financia...

We discuss an information theoretic approach for categorizing and modeling dynamic processes. The approach can learn a compact and informative statistic which summarizes past states to predict future observations. Furthermore, the uncertainty of the prediction is characterized nonparametrically by a joint density over the learned statistic and present observation. We discuss the application of ...

A wavelet approach is presented for estimating a partially linear model (PLM). We find an estimator of the PLM by minimizing the square of the l2 norm of the residual vector with penalizing the l1 norm of the wavelet coefficients of the nonparametric component. This approach, an extension of the wavelet approach for nonparametric regression problems, avoids the restrictive smoothness requiremen...

In this article we develop a nonparametric Bayesian approach to prediction for the M/G/1 queue, focusing on the imbedded semiMarkov process of the queue at the departure times. Our approach is motivated by queues with a large number of data points and highfrequency systems, where times consuming MCMC/ABC algorithms might be infeasible and a nonparametric approach is desirable to avoid parametri...

Estimation of unknown parameters and functions involved in complex nonlinear econometric models is a very important issue. Existing estimation methods include generalised method of moments (GMM) by Hansen (1982) and others, efficient method of moments (EMM) by Gallant and Tauchen (1997), Markov chain Monte Carlo (MCMC) method by Chernozhukov and Hong (2003), and nonparametric simulated maximum ...