References
- Asar, O., Ilk, O., Dag, O. (2017). Estimating Box-Cox power transformation parameter via goodness-of-fit tests. Communications in Statistics - Simulation and Computation 46(1):91–105.
- Bartlett, M. S. (1937). Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London, Series A 160:268–282.
- Box, G. E. P., Cox, D. R. (1964). An analysis of transformations (with discussion). Journal of Royal Statistical Society. Series B (Methodological) 26(2):211–252.
- Cheng, C. (1992). Optimal Sampling for Traffic Volume Estimation. Unpublished Ph.D. dissertation, University of Minnesota, Carlson School of Management.
- Dag, O., Asar, O., Ilk, O. (2014). A methodology to implement Box–Cox transformation when no covariate is available. Communications in Statistics–Simulation and Computation 43(7):1740--1759.
- Dag, O., Asar, O., Ilk, O. (2015). AID: Estimation of Box-Cox Power Transformation Parameter. R Package Version 1.5. Available at http://CRAN.R-project.org/package=AID.
- Fukuda, K. (2006). Time-series forecast jointly allowing the unit-root detection and the Box-Cox transformation. Communications in Statistics–Simulation and Computation 35(2):419–427.
- Gaudard, M., Karson, M. (2000). On estimating the Box-Cox transformation to normality. Communications in Statistics–Simulation and Computation 29(2):559–582.
- Kleiber, C., Zeileis, A. (2008). Applied Econometrics with R. New York: Springer-Verlag. Available at: http://CRAN.R-project.org/package=AER.
- Neter, J., Kutner, M. H., Nachtsheim, C. J., Wasserman, W. (1996). Applied linear statistical models, 4th edition. Chicago: Richard D. Irwin, Inc.
- Perry, M. B., Walker, M. L. (2015). A prediction interval estimator for the original response when using Box-Cox transformations. Journal of Quality Technology 47(3):278–297.
- Rahman, M. (1999). Estimating the Box-Cox transformation via Shapiro-Wilk W statistic. Communications in Statistics–Simulation and Computation 28(1):223–241.
- Rahman, M., Pearson, L. M. (2008). Anderson-Darling statistic in estimating the Box-Cox transformation parameter. Journal of Applied Probability and Statistics 3(1):45–57.
- R Development Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: http://www.R-project.org.
- Shang, H. L. (2015). Selection of the optimal Box-Cox transformation parameter for modelling and forecasting age-specific fertility. Journal of Population Research 32(1):69–79.
- Shapiro, S. S., Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika 52(3/4):591–611.
- Thode, H. C. (2002). Testing For Normality. New York: Marcel Dekker.
- Vorapongsathorn, T., Taejaroenkul, S., Viwatwongkasem, C. (2004). A comparison of type I error and power of Bartlett's test, Levene's test and Cochran's test under violation of assumptions. Songklanakarin Journal of Science and Technology 26(4):537–547.
- Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag.
- Winkelmann, R., Boes, S. (2009). Analysis of Microdata. 2nd ed. Berlin and Heidelberg: Springer-Verlag.