scholarly journals Mean-Variance Model for Optimal Multinational Project Adjustment and Selection

2013 ◽  
Vol 380-384 ◽  
pp. 4809-4814
Author(s):  
Xiao Li Su ◽  
Xiao Xia Huang
Keyword(s):  
Random Variables ◽  
Cash Flows ◽  
Selection Model ◽  
Selection Problem ◽  
Variance Model ◽  
Numerical Example ◽  
Mean Variance ◽  
Selection Of

This paper discusses the multinational project adjustment and selection problem in which project parameters are regarded as random variables. In the paper, adjustment of foreign existing projects and selection of the new foreign projects are considered simultaneously. Typical cash flows and value sources of foreign projects are introduced and a mean-variance optimal multinational adjustment and selection model is proposed. As an illustration, a numerical example is also presented.

scholarly journals A Mean-Variance Portfolio Selection Model with Interval-Valued Possibility Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yunyun Sui ◽  
Jiangshan Hu ◽  
Fang Ma
Keyword(s):  
Portfolio Selection ◽  
Return Rate ◽  
Selection Model ◽  
Expected Return ◽  
Interval Numbers ◽  
Numerical Example ◽  
Mean Variance Portfolio ◽  
Portfolio Selection Model ◽  
Mean Variance ◽  
Interval Valued

In recent years, fuzzy set theory and possibility theory have been widely used to deal with an uncertain decision environment characterized by vagueness and ambiguity in the financial market. Considering that the expected return rate of investors may not be a fixed real number but can be an interval number, this paper establishes an interval-valued possibilistic mean-variance portfolio selection model. In this model, the return rate of assets is regarded as a fuzzy number, and the expected return rate of assets is measured by the interval-valued possibilistic mean of fuzzy numbers. Therefore, the possibilistic portfolio selection model is transformed into an interval-valued optimization model. The optimal solution of the model is obtained by using the order relations of interval numbers. Finally, a numerical example is given. Through the numerical example, it is shown that, when compared with the traditional possibilistic model, the proposed model has more constraints and can better reflect investor psychology. It is an extension of the traditional possibilistic model and offers greater flexibility in reflecting investor expectations.

Selection of Independent Proposals with Random Cash Flows and Limited Total Investment Capitals

2011 ◽  
Vol 90-93 ◽  
pp. 3193-3198
Author(s):  
Ya Fei Jiang
Keyword(s):  
General Model ◽  
Random Variables ◽  
Cash Flows ◽  
Decision Criteria ◽  
Random Condition ◽  
Investment Capital ◽  
Total Investment ◽  
The Common ◽  
Selection Of

Data and parameters of the proposals being selected may be not assured. This uncertainty of data and parameters can be expressed as random variables. The paper is about how to use random decision criteria to select independent proposals where investment capitals available are limited and cash flows or parameters are random variables. The paper first simply introduces the decision criteria on random condition, and gives the common methods to get these values of decision criteria. Then the general model of independent proposals decision with limited capital under random condition, and the solutions of proposals selection are put forward. In the last section a numeric example is given to show how to use software to select independent proposals with random cash flows and limited total investment capital.

Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko
Keyword(s):  
Probability Density ◽  
Optimal Parameter ◽  
Random Variables ◽  
Kernel Functions ◽  
Independent Random Variables ◽  
Functional Dependencies ◽  
Approximation Properties ◽  
Probability Density Estimation ◽  
Multidimensional Probability ◽  
Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

Pathogenic Genes Selection Model of Genetic Disease based on Network Motifs Slicing Feedback

2019 ◽  
Vol 16 (5) ◽  
pp. 392-401
Author(s):  
Shengli Zhang ◽  
Zekun Tong ◽  
Haoyu Yin ◽  
Yifan Feng
Keyword(s):  
Genetic Disease ◽  
Drug Targets ◽  
Selection Model ◽  
Network Motifs ◽  
Gene Set ◽  
Pathway Network ◽  
Gene Pathway ◽  
Pathogenic Genes ◽  
Pathogenic Gene ◽  
Selection Of

Background: Finding the pathogenic gene is very important for understanding the pathogenesis of the disease, locating effective drug targets and improving the clinical level of medical treatment. However, the existing methods for finding the pathogenic genes still have limitations, for instance the computational complexity is high, and the combination of multiple genes and pathways has not been considered to search for highly related pathogenic genes and so on. Methods: We propose a pathogenic genes selection model of genetic disease based on Network Motifs Slicing Feedback (NMSF). We find a point set which makes the conductivity of the motif minimum then use it to substitute for the original gene pathway network. Based on the NMSF, we propose a new pathogenic genes selection model to expand pathogenic gene set. Results: According to the gene set we have obtained, selection of key genes will be more accurate and convincing. Finally, we use our model to screen the pathogenic genes and key pathways of liver cancer and lung cancer, and compare the results with the existing methods. Conclusion: The main contribution is to provide a method called NMSF which simplifies the gene pathway network to make the selection of pathogenic gene simple and feasible. The fact shows our result has a wide coverage and high accuracy and our model has good expeditiousness and robustness.

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