In order to study the association of HLA-A, -B and/or DRB1, DQB1 and the nasopharyngeal carcinoma (NPC), 141 patients affected with NPC were typed for the HLA class I by serology method of microlymphocytotoxicity. Among these patients 101 were genotyped for HLA class II system by the PCR-SSP technique. HLA typing results were compared to those of 116 controls. We found that the HLA-A31 and -A33 antigens were significantly more expressed in patients than in the controls (P = 0.016 and 0.010, respectively) and the HLA-A19 antigen, was significantly more frequent in patients when compared to the controls (P = 0.007). The HLA-DRB1*03 and DRB1*13 alleles were significantly more frequent in patients as compared to the controls. The DRB1*01 allele was expressed with a frequency of 20.69% in the controls whereas it was only detected in 3.96% of the NPC patients. Furthermore, the DQB1*05 allele was expressed at a frequency which was significantly less important in affected patient (P = 0.03), whereas, the DQB1*02 allele was more frequent in patients (P = 0.643 x 10(-4)). Thus our study revealed a significant increase of HLA-A31, A33, A19, B16, B53 and DRB1*03, DRB1*13 and DQB1*02 alleles in our patients. These markers could play a predisposing role in the development of NPC. In contrast, a decrease of HLA-B14, -B35 and DRB1*01 and DQB1*05 alleles was found suggesting a likely protective effect.
The DownSide Risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical Mean-Variance model concerning the asymmetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developed a new recursive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents some discontinuity and is not very smooth. In order to overcome that, Athayde (2003) proposed a Mean Kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which continuous observations are available. In this paper, Athayde model is reformulated and clarified. Then, taking advantage on the robustness of the median, another nonparametric approach based on Median Kernel returns estimation is proposed in order to construct a portfolio frontier. A new version of Athayde's algorithm will be exhibited. Finally, the properties of this improved portfolio frontier are studied and analysed on the French Stock Market.
In this paper, we consider the problem of portfolio optimization. The risk will be measured by conditional variance or semivariance. It is known that the historical returns used to estimate expected ones provide poor guides to future returns. Consequently, the optimal portfolio asset weights are extremely sensitive to the return assumptions used. Getting informations about the future evolution of different asset returns, could help the investors to obtain more efficient portfolio. The solution will be reached under conditional mean estimation and prediction. This strategy allows us to take advantage from returns prediction which will be obtained by nonparametric univariate methods. Prediction step uses kernel estimation of conditional mean. Application on Chinese and American markets are presented and discussed.
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