Anesthetizing the Fibrillating Heart.

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A new estimation method for multivariate Markov chain model with application in demand predictions

In this paper, we propose a new estimation method for the parameters of a multivariate Markov chain model. In the new method, we calculate the correlations of the sequences first and establish multivariate Markov chain models for those positively correlated sequences. The parameters are estimated by minimizing the error of prediction. We apply the method to demand predictions for a soft-drink company in Hong Kong. Numerical experiments are given to show the effectiveness of our proposed method. © 2010 IEEE.published_or_final_versionThe 3rd International Conference on Business Intelligence and Financial Engineering (BIFE 2010), Hong Kong, 13-15 August 2010. In Proceedings of the 3rd BIFE, 2010, p. 126-13

ClinkNotes: Towards a Corpus-Based, Machine-Aided Programme of Translation Teaching

Le présent article fait l’état des lieux d’un projet pilote relatif à la création d’une plateforme conçue pour l’enseignement de la traduction ou la formation bilingue, à grande échelle, aux études supérieures. Bien que les premiers textes utilisés dans le cadre du projet soient en anglais et en chinois, le programme, ClinkNotes, offre la possibilité de prendre en charge des corpus parallèles de n’importe quelle paire de langues. L’article débute par un bref survol de l’application des corpus à la traductologie en lien avec la formation professionnelle en traduction. Puis les caractéristiques du programme (cadre théorique, méthode d’annotation et fonctionnement) sont présentées, ainsi que la manière dont il comble les impératifs pressants de la profession. Les perspectives futures d’amélioration du programme sont également discutées.This article presents a report on a pilot project designed to construct a platform for large-scale teaching of translation or bilingual training at tertiary level. The programme, ClinkNotes, has the potential of accommodating parallel corpora of any language pairs, although the primary data used in this project are in English and Chinese. The report begins with a brief overview of the development of corpus-based approach to translation studies in relation to that of translation teaching as a profession. It then proceeds to describe the actual design (i.e., the theoretical framework, the methodology of annotation, and the simple execution of the software programme), and how it helps to cater to the pressing needs of the profession. The prospects of further development of the programme are also discussed

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formula are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The application is illustrated with a data set on health care demand in Germany. The proposed techniques have been implemented in an open-source R package mpath

Asset allocation under regime-switching models

We discuss an optimal asset allocation problem in a wide class of discrete-time regime-switching models including the hidden Markovian regime-switching (HMRS) model, the interactive hidden Markovian regime-switching (IHMRS) model and the self-exciting threshold autoregressive (SETAR) model. In the optimal asset allocation problem, the object of the investor is to select an optimal portfolio strategy so as to maximize the expected utility of wealth over a finite investment horizon. We solve the optimal portfolio problem using a dynamic programming approach in a discrete-time set up. Numerical results are provided to illustrate the practical implementation of the models and the impacts of different types of regime switching on optimal portfolio strategies. © 2012 IEEE.published_or_final_versio