Bireysel Emeklilik Fonlarının Scheffé Ridge ve Çoklu Regrasyon Modelleri ile İncelenmesi

Özet: Günümüzde bileşenlerin miktarlarına bağlı olan bağımsız değişkenli denemeler ve karışımı oluşturan bileşenlerin miktarlarına bağlı olmaksızın, oranlarına bağlı olan karma denemeler yaygın olarak kullanılmaktadır. 1958 yılında Scheffe' nin ortaya çıkardığı karma denemeler tıp, kimya, gıda gibi bir çok alana uygulanmıştır. Farklı bir uygulama olarak bireysel emeklilik fonları, içerikleri çeşitli yatırım araçlarının belirli oranlarda kullanılması ile oluştuğundan Scheffe karma denemeleri için uygun yapılar oluşturmaktadır. Bu çalışmada, bireysel emeklilik fonlarını oluşturan yatırım araçları bileşenlerinin bağıl oranları kullanılarak Scheffe karma denemeler yöntemi ile fonların yatırım araçlarına bağlılığının modellenmesi yapılmış ve üç farklı fon için durum incelenmiştir. Her modele Ridge ve çoklu regresyoıt uygulanarak sonuçlar değerlendirilmiştir.

Examinin Private Pension Plans With Scheffé and Multiple Regression Models

Abstract: Statistical experiment in which the response is assumed to depend on the amount of mixture and a designed experiment in which the response is assumed to depend only on the relative proportions of the ingredients present in the mixture and not on the amount of the mixture are both commonly used today. After H. Scheffe introduced the pioneering article on designed experiments in 1958, many researches in medicine, chemistry, food and so on have been developed on mixture experiments. As a different application, since the private pension plans are the combination of ratios of various investment instruments, they have suitable structure for Scheffe models. In this work, using the proportions of the components of private pension plans, dependency offunds 011 investment instruments was modeled by Scheffe models and three funds were examined. The Ridge regression and multiple regressions were also applied to each model and the results were evaluated.

Kaynakça

[1]Juran, J.M. & Gryna, F.M. (1981). Planning and Analysis of Quality. 2nd Ed. New York: McGraw-Hill.

[2]Cornell, J.A. (1990). Experiments With Mixtures. 2nd Ed. New York: John Wiley & Sons.

[3]Draper, N.R. & Pulkelsheim, F. (2002). Generaized Ridge Analysis Under Linear Restrictions with particular Applications to Mixture Experiments Problems. Technometrics, 44(3), 250-258. (www.math.uni-augsburg.de/stochastik/pukelsheim/2002a.pdf) [07.04.2005],

[4]Scheffe, H. (1958). Experiments with Mixtures. Journal of The Royal Statistical Society. Series B, 20(2), 344-360.

[5]Crosier, B.R. (1984). Mixture Experiments: Geometry and Pseudocomponents. Technometrics, 26(3), 209-216.

[6]Steiner, S.H. & Hamada, M. (1997). Making Mixture Robust to Noise Factors and Mixing Measurement Errors. Journal of Quality Technology, 29(4), 441-450, (www.stats.uwaterloo.ca/~shsteine/papers/mix.pdf). [07.04.2005],

[7]Piepel, G.F. (1983). Defining Consistent Regions in Mixture Experiments. Technometrics, 25(1), 97-101.

[8]Claringbold, P.J. (1955). Use of the Simplex Design in the Study of the Join Action of Related Hormones. Biometrics, 11(2), 174-185.

[9]Gorman, J.W. (1970). Fitting Equations to Mixture Data With Restraints on Compositions. Journal of Quality Technology, 2(4), 186-194.

[10]Marquart, D.W. (1970). Generalized Inverses Ridge Regression Biased Linear Estimation and Nonlinear Estimation. Technometrics, 12(3), 591-612.

[11]Orhunbilge, N. (1996). Uygulamalı Regresyon ve Korelasyon Analizi. İ.Ü. İşletme Fakültesi Yayın No: 267, İstanbul: İ. Ü. İşletme Fakültesi İşletme İktisadı Yayın No: 159.

[12]Myers, R.H. (1990). Classical and Modem Regression with Applications. 2nd Ed. Boston: PWS Kent.

[13]Freund, R.J. & Littell, R.C. (1986). 5145 System Linear Regression. (Ed.: Cary, N.C.). USA: SAS Institute Inc.

[14]Petraitis, P.S. (1996). How can we compare the importance of ecological processes if we never ask, 'Compared to what?1 Issues and Perspectives in Experimental Ecology (Eds.: Resitarits, W. & Bernardo, J.). New York: Oxford University Press.

[15]Draper, N.R. & Smith, H. (1981). Applied Regression Analysis. New York: John Wiley & Sons, Inc.

[16]Freund, R.P. & Minton, P.D. (1979). Regression Methods: A Tool for Data Analysis. New York: Marcel Dekker, Inc.

[17]Wetherill, G.B.; Duncombe, P.; Kollerstrom, J.; Kenward, M.; Paul, S.R. & Vowden, B.J. (1986). Regression Analysis with Applications. London: Chapman and Hall.

[18]Berry, W.D. & Feldman, S. (1985). Multiple Regression in Practice. Beverly Hills: Sage Publications.

[19]Laviolette, M. (1994). Linear regression: The computer as a teaching tool. Journal of Statistical Education, 2(2), (http://www.amstat.org/publications/jse/v2n2/laviolette.ht ml). [13.02.2005],

[20]Hoerl, A.E. (1959). Optimum Solution of Many Variables Equations. Chemical Engineering Progress, 55(11), 69-78.

[21]Hoerl, A.E. (1962). Applications of Ridge Analysis to Regression Problems. Chemical Engineering Progress. 58(3), 54-59.

[22]Hoerl, A.E. (1964). Ridge Analysis. Chemical Engineering Progress Symposium Series, 60(1), 67-77.

[23]Hoerl, R.W. (1985). Ridge Analysis 25 Years Later, The American Statisticia, 39(3), 186-192. (http://www.stat.ncsu.edu/info/jse/homepage.html). [07.04.2005].

[24]Draper, N.R. (1963). Ridge Analysis of Response Surfaces. Technometrics, 5(3), 469-479.

[25]Myers, R.H., & Carter, W.H.Jr. (1973). Response Surface Techniques for Dual Response Systems. Technometrics. 15(2), 301-317.

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