ASSESSMENT OF LOCAL INFLUENCE FOR GENERALIZED CORRELATION COEFFICIENTS

In this paper, we study local influence analysis for Zhang's generalized correlation coefficients and Hotelling's generalized correlation coefficient by using approach. of local influence analysis suggested by Shi (1991), i.e., generalized influence function (GIF) and generalized Cook distance (GCD). An example is given to illustrate our results.

Generalized Ridge and Principal Correlation Estimator of the Regression Parameters and Its Optimality

In this paper,we propose a new biased estimator of the regression parameters,the generalized ridge and principal correlation estimator.We present its some properties and prove that it is superior to LSE(least squares estimator),principal correlation estimator,ridge and principal correlation estimator under MSE(mean squares error) and PMC(Pitman closeness) criterion,respectively.

Time-consistent investment and reinsurance strategies for insurers under multi-period mean-variance formulation with generalized correlated returns

The existing literature on investment and reinsurance is limited to the study of continuous-time problems,while discrete-time problems are always ignored by re-searchers.In this study,we first discuss a multi-period investment and reinsurance opti-mization problem under the classical mean-variance framework.When the asset returns with a serially correlated structure,the time-consistent investment and reinsurance strategies are acquired via backward induction.In addition,we propose an alternative time-consistent mean-variance optimization model that contrasts with the classical mean-variance model,and the corresponding optimal strategy and value function are also derived.We find that the investment and reinsurance strategies are both independent of the current wealth for the above two optimization problems,which coincides with the conclusion presented in the continuous-time problems.Most importantly,the above in-vestment strategies with serially correlated structures are both conditional mean-based strategies,rather than unconditional ones.Finally,we compare the investment and rein-surance strategies suggested above based on the simulation approach,to shed light on which investment-reinsurance strategies are more suitable for insurers.

Measurement-based solution for low frequency oscillation analysis

This paper presents a measurement-based solution for low frequency oscillation(LFO) analysis in both real time monitoring and off-line case study. An online LFO property discrimination method is developed first,which alternately uses empirical mode decomposition(EMD)/Hilbert transform(HT) and square calculation to process the measurement data. The method magnifies the variation trend of oscillating variables to accurately discriminate the property of the oscillation. Subsequently, an oscillation source locating method for the forced oscillation(FO) and a strongly correlated generator identification method for the weak damping oscillation(WDO) are proposed. Finally, numerical study results on a test system of the isolated Changdu grid in Tibet validate the proposed methods.

Fuzzy reasoning model based on universal combination operation model

The universal combination operation model is a comprehensive decision model of continuous-valued logic. It overcomes limitations of the scope of operations of reasoning operators in the current comprehensive decision-making system. This article discusses relationship of mutual information and general correlation coefficient and gives the corresponding rules of them, the optimal matching operator is selected to complete fuzzy decision according to mutual information between candidate attributes. The relationship of mutual information between attributes and generalized correlative coefficient provides the principle to select the matching operator. According to the results of experiment, it is more reasonable to enhance classified precision effectively. There is a certain application value of the article＇s method.

Regularized canonical correlation analysis with unlabeled data

In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.