Aitken, M. (1987) “Modelling Variance Heterogeneity in Normal Regression Using GLIM,“ Applied Statistics 36:3, 332–339.

Anderson, T. W. (1958) An Introduction to Multivariate Statistical Analysis. New York: John Wiley & Sons.

Belsley, D.A., Kuh, E., and Welsch, R.E. (1980), Regression Diagnostics, New York: John Wiley and Sons.

Benjamini, Yoav and Hochberg, Yosef (1995). “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,“ Journal of the Royal Statistical Society, Series B, 57, 289–300.

Box, G. E. P. (1954). “Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, II: Effects of Inequality of Variance and Correlation Between Errors in the Two-Way Classification,“ Annals of Mathematical Statistics, 25, 484-498.

Box, G.E.P. and Meyer, R.D. (1986), “An Analysis of Unreplicated Fractional Factorials,“ Technometrics 28, 11–18.

Box, G.E.P. and Meyer, R. D. (1993), “Finding the Active Factors in Fractionated Screening Experiments.“, Journal of Quality Technology Vol.25 #2: 94–105.

Box, G.E.P., Hunter,W.G., and Hunter, J.S. (1978), Statistics for Experimenters, New York: John Wiley and Sons, Inc.

Burnham, K.P. and Anderson, D.R. (2002), Model Selection And Multimodel Inference: A Practical Information Theoretic Approach. Springer, New York.

Burnham, K.P. and Anderson, D.R. (2004), “Multimodel Inference: Understanding AIC and BIC in Model Selection,“ Sociological Methods and Research, 33: 261-304.

Burnham, K.P., Andersen, D.R., and Huyvaert, K.P. (2011), “AIC Model Selection and Multimodel Inference in Behavioral Ecology: Some Background, Observations, and Comparisons,“ Behavioral Ecology and Sociobiology, 65: 23-35.

Carroll, R.J. and Ruppert, D. (1988), Transformation and Weighting in Regression, New York: Chapman and Hall.

Chiles, J.-P. and Delfiner, P. (2012), Geostatistics: Modeling Spatial Uncertainty, Second Edition, Wiley: New Jersey.

Cobb, G.W. (1998), Introduction to Design and Analysis of Experiments, Springer-Verlag: New York.

Cole, J.W.L. and Grizzle, J.E. (1966), “Applications of Multivariate Analysis of Variance to Repeated Measures Experiments,“ Biometrics, 22, 810–828.

Conover, W.J. (1999), Practical Nonparametric Statistics, 3rd Edition, New York: John Wiley and Sons, Inc.

Cook, R.D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: Chapman and Hall.

Cook, R.D. and Weisberg, S. (1983), “Diagnostics for heteroscedasticity in regression“ Biometrika 70, 1–10.

Cornell, J.A. (1990), Experiments with Mixtures, Second Edition, New York: John Wiley and Sons.

Cox, D.R. (1972), “Regression Models and Life Tables,“ Journal of the Royal Statistical Society, Series B, 20, 187–220, with discussion.

Cox, D.R. and Snell, E.J., (1989), Analysis of Binary Data, Second Edition, New York: Chapman and Hall.

Cressie, N.A.C. (1993), Statistics for Spatial Data, Revised Edition, John Wiley & Sons.

Daniel, C. (1959), “Use of Half–normal Plots in Interpreting Factorial Two–level Experiments,“ Technometrics, 1, 311–314.

Dunnett, C.W. (1955), “A multiple comparison procedure for comparing several treatments with a control“ Journal of the American Statistical Association, 50, 1096–1121.

Dwass, M. (1955), “A Note on Simultaneous Confidence Intervals,“ Annals of Mathematical Statistics 26: 146–147.

Efron, B. (1977), “The Efficiency of Cox’s Likelihood Function for Censored Data,“ Journal of the American Statistical Association, 72, 557–565.

Farebrother, R.W. (1981), “Mechanical Representations of the L1 and L2 Estimation Problems,“ Statistical Data Analysis, 2nd Edition, Amsterdam, North Holland: edited by Y. Dodge.

Fieller, E.C. (1954), “Some Problems in Interval Estimation,“ Journal of the Royal Statistical Society, Series B, 16, 175-185.

Goodnight, J.H. (1978), “Tests of Hypotheses in Fixed Effects Linear Models,“ SAS Technical Report R–101, Cary: SAS Institute Inc, also in Communications in Statistics (1980), A9 167–180.

Goodnight, J.H. and W.R. Harvey (1978), “Least Square Means in the Fixed Effect General Linear Model,“ SAS Technical Report R–103, Cary NC: SAS Institute Inc.

Goos, P. and Jones, B. (2011), Optimal Design of Experiments: A Case Study Approach, John Wiley and Sons.

Greenhouse, S. W. and Geisser, S. (1959). “On Methods in the Analysis of Profile Data.“ Psychometrika, 32, 95–112.

Harrell, F. (1986), “The Logist Procedure,“ SUGI Supplemental Library User’s Guide, Version 5 Edition, Cary, NC: SAS Institute Inc.

Harvey, A.C. (1976), “Estimating Regression Models with Multiplicative Heteroscedasticity,“ Econometrica 44–3 461–465.

Hayter, A.J. (1984), “A proof of the conjecture that the Tukey–Kramer multiple comparisons procedure is conservative,“ Annals of Mathematical Statistics, 12 61–75.

Heinze, G. and Schemper, M. (2002), “A Solution to the Problem of Separation in Logistic Regression,“ Statistics in Medicine 21:16, 2409–2419.

Hocking, R.R. (1985), The Analysis of Linear Models, Monterey: Brooks–Cole.

Hoenig, J. M. and Heisey, D.M. (2001), “The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis,“ American Statistician, 55:1 pp. 19-24.

Hoerl, A.E. (1962), “Application of Ridge Analysis to Regression Problems,“ Chemical Engineering Progress, 58, 54-59.

Hoerl, A.E. and Kennard, R.W. (1970), “Ridge Regression: Applications to Nonorthogonal Problems,“ Technometrics, 12:1, 69-82.

Hsu, J. C. (1996), Multiple Comparisons: Theory and Methods, CRC Press.

Hsu, J. C. (1992), “The Factor Analytic Approach to Simultaneous Inference in the General Linear Model,“ Journal of Computational and Graphical Statistics, 1:2, 151-168.

Huber, P.J. and Ronchetti, E.M. (2009), Robust Statistics, second edition, John Wiley and Sons.

Hui, F., Warton, D. and Foster, S. (2015), “Tuning Parameter Selection for the Adaptive Lasso Using ERIC,“ Journal of the American Statistical Association, 110:509, 262–269.

Huynh, H. and Feldt, L. S. (1970). “Conditions under which Mean Square Ratios in Repeated Measurements Designs have Exact F-Distributions.“ Journal of the American Statistical Association, 65, 1582–1589.

Huynh, H. and Feldt, L. S. (1976). “Estimation of the Box Correction for Degrees of Freedom from Sample Data in the Randomized Block Split Plot Designs.“ Journal of Educational Statistics, 1, 69–82.

John, P.W.M. (1971), Statistical Design and Analysis of Experiments, New York: Macmillan Publishing Company, Inc.

Kackar, R.N. and Harville, D.A. (1984), Approximations for standard errors of estimators of fixed and random effects in mixed linear models, Journal of the American Statistical Association, 79, 853-862.

Kenward, M.G. and Roger, J.H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983-997.

Kalbfleisch, J.D. and Prentice, R.L. (2002), 2nd Edition, The Statistical Analysis of Failure Time Data, second edition, New York: John Wiley and Sons.

Koenker, R. and Hallock, K.F. (2001), “Quantile Regression,“ Journal of Economic Perspectives, Vol. 15, No. 4, pp. 143-156.

Kramer, C.Y. (1956), “Extension of multiple range tests to group means with unequal numbers of replications,“ Biometrics, 12, 309–310.

Lenth, R.V. (1989), “Quick and Easy Analysis of Unreplicated Fractional Factorials,“ Technometrics, 31, 469–473.

Littell, R.C., Milliken, G.A., Stroup, W.W., Wolfinger, R.D., and Schabenberger, O. (2006), SAS for Mixed Models, 2nd edition, SAS Institute Inc., Cary, NC.

Mardia, K.V., Kent, J.T., and Bibby J.M. (1979). Multivariate Analysis, New York: Academic Press.

McCullagh, P. and Nelder, J.A. (1989), Generalized Linear Models, 2nd Edition, London: Chapman and Hall Ltd.

McCulloch, C.E., Searle, S.R., and Neuhaus, J.M. (2008), Generalized, Linear, and Mixed Models, New York: John Wiley and Sons.

Miller, A.J. (1990), Subset Selection in Regression, New York: Chapman and Hall.

Montgomery, D. C. (1991), “Using Fractional Factorial Designs for Robust Process Development,“ Quality Engineering, 3, 193–205.

Muller, K.E. and Barton, C.N. (1989), “Approximate Power for Repeated–measures ANOVA Lacking Sphericity,“ Journal of the American Statistical Association, 84, 549–555.

Myers, R. H. and Montgomery, D. C. (1995), Response Surface Methodology, New York: John Wiley and Sons.

Nagelkerke, N.J.D. (1991), “A Note on a General Definition of the Coefficient of Determination,“ Biometrika, 78:3 691-692.

Nelder, J.A. and Wedderburn, R.W.M. (1972), “Generalized Linear Models,“ Journal of the Royal Statistical Society, Series A, 135, 370–384.

Nelson, F. (1976), “On a General Computer Algorithm for the Analysis of Model with Limited Dependent Variables,“ Annals of Economic and Social Measurement, 5/4.

Nelson, P. R., Wludyka, P. S., and Copeland, K. A. F. (2005), The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions, SIAM.

Patterson, H. D. and Thompson, R. (1974). Maximum likelihood estimation of components of variance. Proc. Eighth International Biochem. Conf., 197–209.

Poduri, S.R.S. Rao (1997), Variance Components: Mixed Models, Methodologies and Applications (Monographs on Statistics and Applied Probability), New York, Chapman & Hall.

Portnoy, Stephen (1971), “Formal Bayes Estimation with Application to a Random Effects Model“, The Annals of Mathematical Statistics, Vol. 42, No. 4, pp. 1379–1402.

Portnoy, Stephen, and Koenker, Roger (1997), “The Gaussian Hare and the Laplacian Tortoise: Computability of Squared-Error versus Absolute-Error Estimators,“ Statistical Science, Vol. 12, No. 4, pp. 279-300.

Rawlings, J.O. (1988), Applied Regression Analysis: A Research Tool, Pacific Grove CA: Wadsworth and Books/Cole.

Rodriguez, R.N. (1990), “Selected SAS/QC Software Examples, Release 6.06,“ SAS Institute Inc., Cary, NC.

Ruppert, Wand, and Carroll (2003), Semiparametric Regression, Cambridge, United Kingdom: Cambridge University Press.

Sall, J.P. (1990), “Leverage Plots for General Linear Hypotheses,“ American Statistician, 44, (4), 308–315.

Sánchez, L.B., Lachos, V.H., and Labra, V.F. (2013), “Likelihood Based Inference for Quantile Regression Using the Asymmetric Laplace Distribution,“ Journal of Statistical Computation and Simulation, Vol. 81, pp. 1565-1578.

SAS Institute Inc. (1987), SAS/STAT® Guide for Personal Computers, Version 6 Edition, Cary NC: SAS Institute Inc.

Satterthwaite, F.E., (1946), “An Approximate Distribution of Estimates of Variance Components,“ Biometrics Bulletin, 2, 110–114.

Scheffé, H. (1958) “Experiments with Mixtures“, Journal of the Royal Statistical Society B v20, 344–360.

Schuirmann, D. J. (1987), “A Comparison of the Two One-sided Tests Procedure and the Power Approach for Assessing the Equivalence of Average Bioavailability,“ J. Pharmacokin. Biopharm., 15, 657–680.

Searle, S. R, Casella, G. and McCulloch, C. E. (1992) Variance Components. New York: John Wiley and Sons.

Seber, G.A.F. (1984), Multivariate Observations, New York: John Wiley and Sons, 413–416.

Singer, J.D. (1998), “Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models,“ Journal of Educational and Behavioral Statistics, 24:4, 323-355.

Snedecor, G.W. and Cochran, W.G. (1967), Statistical Methods, Ames, Iowa: Iowa State University Press.

Stone, C. and Koo, C.Y. (1985), “Additive Splines in Statistics,“ Proceedings of the Statistical Computing Section, 45-48, Amer. Statist. Assoc., Washington, DC.

Sullivan, L.M., Dukes, K.A., Losina, E., (1999), “An Introduction to Hierarchical Linear Modelling,“ Statistics in Medicine, 18:7, 855-888.

Taguchi, G. (1976), “An Introduction to Quality Control,“ Nagoya, Japan: Central Japan Quality Control Association.

Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,“ Journal of the Royal Statistical Society, Series B, 58:1, 267-288.

Tukey, J. (1953), “A problem of multiple comparisons,“ Dittoed manuscript of 396 pages, Princeton University.

Walker, S.H. and Duncan, D.B. (1967), “Estimation of the Probability of an Event as a Function of Several Independent Variables,“ Biometrika 54.

Westfall, P.H., Tobias, R.D., Wolfinger, R.D. (2011), Multiple Comparisons and Multiple Tests Using SAS®, Second Edition, SAS Press.

Wilks, S.S. (1938), “The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses,“ Annals of Mathematical Statistics, 9, 60-62.

Wolfinger, R., Tobias, R., and Sall, J. (1994). Computing Gaussian likelihoods and their derivatives for general linear mixed models. SIAM J. Sci. Comput. 15, 6 (Nov. 1994), 1294-1310.

Wright, S.P. and R.G. O'Brien (1988), “Power Analysis in an Enhanced GLM Procedure: What it Might Look Like,“ SUGI 1988, Proceedings of the Thirteenth Annual Conference, 1097–1102, Cary NC: SAS Institute Inc.

Zou, H. and Hastie, T. (2005), “Regularization and Variable Selection via the Elastic Net,“ Journal of the Royal Statistical Society, Series B, 67:2, 301-320.