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ÇOKLU DOĞRUSAL BAĞLANTI HALİNDE ENKÜÇÜK KARELER TEKNİĞİNİN ALTERNATİFİ YANLI TAHMİN TEKNİKLERİ VE BİR UYGULAMA

Journal Name:

  • Zonguldak Karaelmas Üniversitesi Sosyal Bilimler Dergisi

Publication Year:

  • 2005

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
The purpose of this paper is to examine the effectiveness of applying biased estimation techniques (RR and PC) over Least Squares (LS) technique. For this purpose, the relative predictive validity of three regression techniques was compared using the weight data to study the linear relation of dependent variable to predictor variables. It was hypothesized that, given the high degree of multicolinearity of the predictor variables, biased estimation techniques would provide more stabilized coefficient and less standard errors than would the LS technique.
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Abstract (Original Language): 
Bu çalışmanın amacı, beden ağırlığının tahmin edilmesinde yanlı tahmin tekniklerinin [Ridge Regression (RR) ve Principal Component (PC)] enküçük kareler [Least Squares (LS)] tekniğine karşı etkinliğini araştırmaktır. Bu amaçla beden ağırlığı ile açıklayıcı değişkenler arasındaki doğrusal ilişkinin tahmininde LS ve yanlı tahmin tekniklerinin (RR ve PC) göreceli tahmin geçerlilikleri karşılaştırılmaktadır. Araştırmada, bağımsız değişkenler arasındaki yüksek çoklu doğrusal bağlantı problemine dayanarak RR ve PC tekniklerinin LS tekniğine göre daha düşük standart hatalı, durağan ve kuramsal beklentilere uygun tahminler sağlayacağı beklenmiştir.
FULL TEXT (PDF): 
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  • 1
105-126
  • Turkish

REFERENCES

References: 

Anderson, Björn (1998); Scandinavian Evidence on Growth and Age Structure, ESPE
1997 Conference at Uppsala University.
Darlington, R. B. (1978); “Reduced Variance Regression,” Psychological Bulletin, 85,
s. 1283-1255.
Dempster, A. P., M. Schatzoff, and N. Wermuth (1977); “A Simulation Study of
Alternatives to Ordinary Least Square,” Journal of American Statistical
Association, 72, s. 77-91.
Draper, N. R., and H. Smith (1981); Applied Regression Analysis, John Willey, NY.
Faden, V. B. (1978); Shrinkage in Regression and Ordinary Least Squares Multiple
Regression Estimators, Yayınlanmamış Doktora Tezi, University of Maryland.
Gujarati, D. N. (1995); Basic Econometrics, 3rd Ed., McGraw-Hill, New York.
Hoerl, A. E. and Kennard R.W. (1970); “Ridge Regression: Biased Estimation for
Nonorthogonal Problems,” Technometrics, 12, s. 69-82.
Kleinbaum D. G., Lawrence L. Kupper and Keith E. Muller (1988); Applied Regression
Analysis and Other Multivariable Methods, Duxbury Press, New Jersey.
Maxwell, Scott E. (2000); “Sample Size in Multiple Regression Analysis,”
Psychological Methods, Vol. 5, No: 4, s. 435-458.
Myers, R. H. (1990); Classical and Modern Regression with Applications,
Massachusetts: PWS-Kent Publishing Company, Boston.
Neter, J., W. Wasserman and M. Kunter (1990); Applied Linear Statistical Models, 3rd
Ed., New Jersey.
NCSS Inc. (2001); NCSS User Guide 2001, Kaysville, NCSS Inc.
Orhunbilge, Neyran (2000); Uygulamalı Regresyon ve Korelasyon Analizi, Avcıol-
Basım Yayın, İstanbul.
Price, B. (1979); “Ridge Regression: Application to Nonexperimental Data,”
Psychological Bulletin, 84, s. 759-766.
Rawlings, J. O. (1998); Applied Regression Analysis: A Research Tool, California.
Shin, Kilman (1996); SPSS Guide for DOS Version 5 and Windows 6.1.2, 2nd Ed., Irwin,
Chicago.
SPSS Inc, (1999); SPSS® Base 10 Application Guide, Chicago: SPSS Inc.
Tracey, T. J., W. E. Sedlacek and R. D. Miras (1983); “Applying Ridge Regression to
Admissions Data by Race,” College and University, 58, s. 313-318.
Vinod, H. D. (1995); “Double Bootstrap for Shrinkage Estimators,” Journal of
Econometrics, 68, s. 287-302.

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