Addressing multicollinearity in semiconductor manufacturing

Quality and Reliability Engineering International

Volumn 27, Issue 6, 2011, Pages 843-854

  Chang, Yu Ching ^a   Mastrangelo, Christina ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

multicollinearity; principal components regression; variable elimination; variance inflation factor

Indexed keywords

CAUSAL RELATIONSHIPS; INFLATION FACTORS; INPUT/OUTPUT; MEAN SQUARE; MULTICOLLINEARITY; ORTHOGONAL TRANSFORMATIONS; PAIRWISE CORRELATION; PREDICTION MODEL; PRINCIPAL COMPONENT REGRESSION; PRINCIPAL COMPONENTS; RIDGE REGRESSION; SEMICONDUCTOR MANUFACTURING; VARIABLE ELIMINATION;

MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; SEMICONDUCTOR DEVICE MANUFACTURE;

REGRESSION ANALYSIS;

EID: 80052919208     PISSN: 07488017     EISSN: 10991638     Source Type: Journal    
DOI: 10.1002/qre.1173     Document Type: Article

References (20)

1. Farrar DE, Glauber RR,. Multicollinearity in regression analysis: The problems revisited. Review of Economics and Statistics 1967; 49: 92-107.
2. Montgomery DC, Peck EA, Vining G,. Introduction to Linear Regression Analysis (4th edn). Wiley: Hoboken, NJ, 2006.
3. Mason CH, Perreault WD,. Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research 1991; 28: 268-280.
4. Mason RL, Gunst RF, Webster JT,. Regression analysis and problems of multicollinearity. Communication in Statistics 1975; 4: 277-292.
5. Tabachnick BG, Fidell LS,. Testing hypotheses in multiple regression. Using Multivariate Statistics. Allyn and Bacon: Boston, 2001.
6. Marquardt DW,. Generalized inverse, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics 1970; 12: 591-612.
7. O'Brien RM,. A caution regarding rules of thumb for variance inflation factors. Quality and Quantity 2007; 41: 673-690.
8. Belsey DA, Kuh E, Welsch RE,. Regression Diagnostics. Wiley: Hoboken, NJ, 1980.
9. Weisberg S,. Applied Linear Regression. Wiley: Hoboken NJ, 2005.
10. Massy WF,. Principal component regression in exploratory statistical research. Journal of American Statistical Association 1965; 60: 234-256.
11. Farebrother RW,. Gram-Schmidt regression. Applied Statistics 1974; 23: 470-476.
12. Hoerl AE, Kennard RW,. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970; 12: 55-67.
13. Webster JT, Gunst RF, Mason RL,. Latent root regression analysis. Technometrics 1974; 16: 513-522.
14. Johnson RA, Wichern DW,. Applied Multivariate Statistical Analysis. Prentice-Hall: Upper Saddle River, NJ, 2002.
15. Hocking RR,. The analysis and selection of variables in linear regression. Biometrics 1976; 32: 1-49.
16. Hoerl AE, Kennard RW, Baldwin KF,. Ridge regression: Some simulations. Communications in Statistics 1975; 4: 105-123.
17. Hoerl AE, Kennard RW,. Ridge regression: Iterative estimation of the biasing parameter. Communications in Statistics 1976; A5: 77-88.
18. Raftery AE, Madigan D, Hoeting JA,. Bayesian model averaging for regression models. Journal of the American Statistical Association 1997; 92: 179-191.
19. Thall PF, Russell KE, Simon RM,. Variable selection in regression via repeated data splitting. Journal of Computational and Graphical Statistics 1997; 6: 416-434.
20. Zou H, Hastie T,. Regularization and variable selection via the elastic net. Journal of Royal Statistics Society B 2005 67: 301-320. (Pubitemid 40465877)