Crucial problems in regression modelling and their solutions.

[1]  Peter L. Bonate,et al.  The Effect of Collinearity on Parameter Estimates in Nonlinear Mixed Effect Models , 1999, Pharmaceutical Research.

[2]  Neima Brauner,et al.  Considering Error Propagation in Stepwise Polynomial Regression , 1999 .

[3]  Neima Brauner,et al.  Considering precision of experimental data in construction of optimal regression models , 1999 .

[4]  M. Hill,et al.  Age Relationships and Sex Differences in Serum Levels of Pregnenolone and 17-Hydroxypregnenolone in Normal Subjects , 1999, Clinical chemistry and laboratory medicine.

[5]  S. P. Ellis,et al.  Instability of least squares, least absolute deviation and least median of squares linear regression, with a comment by Stephen Portnoy and Ivan Mizera and a rejoinder by the author , 1998 .

[6]  Neima Brauner,et al.  Identifying and removing sources of imprecision in polynomial regression , 1998 .

[7]  Desire L. Massart,et al.  MULTIPLE OUTLIER DETECTION REVISITED , 1998 .

[8]  Neima Brauner,et al.  Minimizing the effects of collinearity in polynomial regression , 1997 .

[9]  Debasis Sengupta,et al.  On the Roles of Observations in Collinearity in the Linear Model , 1997 .

[10]  R. Stine.  Graphical Interpretation of Variance Inflation Factors , 1995 .

[11]  Robert F. Ling,et al.  K-Clustering as a Detection Tool for Influential Subsets in Regression , 1984 .

[12]  G. V. Kass,et al.  Location of Several Outliers in Multiple-Regression Data Using Elemental Sets , 1984 .

[13]  F. S. Wood.  Comment: Effect of Centering on Collinearity and Interpretation of the Constant , 1984 .

[14]  R. Cook.  [Demeaning Conditioning Diagnostics through Centering]: Comment , 1984 .

[15]  Ronald D. Snee,et al.  Comment: Collinearity Diagnostics Depend on the Domain of Prediction, the Model, and the Data , 1984 .

[16]  R. Gunst.  Comment: Toward a Balanced Assessment of Collinearity Diagnostics , 1984 .

[17]  David A. Belsley.  Demeaning Conditioning Diagnostics through Centering , 1984 .

[18]  Charles E. McCulloch,et al.  [Outlier..........s]: Discussion , 1983 .

[19]  D. Pierce,et al.  Testing normality of errors in regression models , 1982 .

[20]  Philip Prescott,et al.  On the Accuracy of Bonferroni Significance Levels for Detecting Outliers in Linear Models , 1981 .

[21]  S. Weisberg,et al.  Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression , 1980 .

[22]  R. Cook.  Influential Observations in Linear Regression , 1979 .

[23]  S. S. Srivastava,et al.  Correlation in Polynomial Regression , 1979 .

[24]  R. Welsch,et al.  The Hat Matrix in Regression and ANOVA , 1978 .

[25]  K. Berk.  Tolerance and Condition in Regression Computations , 1977 .

[26]  J. F. Gentleman,et al.  Detecting Outliers. II. Supplementing the Direct Analysis of Residuals , 1975 .

[27]  J. T. Webster,et al.  Latent Root Regression Analysis , 1974 .

[28]  F. Hampel.  The Influence Curve and Its Role in Robust Estimation , 1974 .

[29]  A. Hedayat,et al.  Independent Stepwise Residuals for Testing Homoscedasticity , 1970 .

[30]  Donald W. Marquaridt.  Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation , 1970 .

[31]  A. E. Hoerl,et al.  Ridge Regression: Applications to Nonorthogonal Problems , 1970 .