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 TI  Vol.1 No.3 , August 2010
Evaluation of Venture Capital Based on Evaluation Model
Abstract: This paper studies evaluation problem in venture capital. Based on the venture capital and the actual evaluation work, we use an evaluation model proposed by us to evaluate the profitability of enterprises. We establish the impact of investment income and investment risk index system, corresponding to get observational data of the second order indexes. Evaluation model is a kind of generalized linear regression model with convex constraint, in which the dependent variable is unknown and regression coefficients all are calculated in accordance with samples instead of the prior designated. The least squares estimation of the model is given by the interactive projection algorithm between the convex sets, so as to provide a new analysis method for venture capital evaluation index system.
Cite this paper: nullH. Tong, Y. Pan, Y. Ye, S. Lu and H. Liu, "Evaluation of Venture Capital Based on Evaluation Model," Technology and Investment, Vol. 1 No. 3, 2010, pp. 201-204. doi: 10.4236/ti.2010.13023.
References

[1]   Z. W. Zhao, “Research on the Appraisal and Decision- Making in Venture Capital,” Management Science and Engineering (Professional), Tianjin University, Doctoral Dissertation, 2005.

[2]   Z. H. Wang and L. Li, “Comparison of Venture Capital Assessment,” Journal of Nanhua University (Social Science Edition), Vol. 8, No. 1, February 2007, pp. 43-45.

[3]   H. Q. Tong, “Evaluation Model and its Iterative Algorithm by Alternating Projection,” Mathematical and Computer Modelling, Vol. 18, No. 8, 1993, pp. 55-60.

[4]   H. Q. Tong, S. J. Zhong, T. Z. Liu and Y. F. Deng, “Biostatistics Algorithm: Evaluation Model with Convex Constraint and its Parameters Estimates,” The 1st Inter- national Conference on Bioinformatics and Biomedical Engineering, 2007, pp. 402-405.

[5]   H. Q. Tong, “Theory of Economics,” Science Press, 2005, pp. 262-264.

[6]   K. T. Fang and S. D. He, “Regression Models with Linear Constraints and Nonnegative Regression Coefficients,” Mathematica Numerica Sinica, Vol. 7, 1985, pp. 97-102.

 
 
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