Box–Cox Transformation in Big Data

References

• Alwall, J., Herquet, M., Maltoni, F., Mattelaer, O., and Stelzer, T. (2011), “MadGraph 5: Going Beyond,” Journal of High Energy Physics, 1106, article number 28.
   Web of Science ®Google Scholar
• Baldi, P., Sadowski, P., and Whiteson, D. (2014), “Searching for Exotic Particles in High-Energy Physics With Deep Learning,” Nature Communications, 5, article number 4308.
   Web of Science ®Google Scholar
• Box, G. E. P., and Cox, D. R. (1964), “An Analysis of Transformations,” Journal of the Royal Statistical Society, Series B, 26, 211–252.
   Google Scholar
• Celik, H. M. (2004), “Forecasting Interregional Commodity Flows Using Artificial Neural Networks: An Evaluation,” Transportation Planning and Technology, 27, 449–467.
   Web of Science ®Google Scholar
• Chen, Y., and Dong, G. (2006), “Regression Cubes With Lossless Compression and Aggregation,” IEEE Transactions on Knowledge and Data Engineering, 18, 1585–1599.
   Web of Science ®Google Scholar
• Dean, J., and Ghemawat, S. (2004), “MapReduce: Simplified Data Processing on Large Clusters,” in OSDI’s 04 Proceedings of the 6th conference on Symposium on Operating Systems Design & Implementation (Vol. 6), pp. 137–150.
   Google Scholar
• Dhillon, P. S., Lu, Y., Foster, D., and Ungar, L. (2013), “New Subsampling Algorithms for Fast Least Squares Regression,” Advances in Neural Information Processing, 26, 360–368.
   Google Scholar
• Drineas, P., Mahoney, M. W., and Muthukrishnan, S. (2006), “Sampling Algorithms for l2 Regression and Applications,” in Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms, Miami, Florida, pp. 1127–1136.
   Google Scholar
• Drineas, P., Mahoney, M. W., Muthukrishnan, S., and Sarlós, T. (2011), “Faster Least Squares Approximation,” Numerische Mathematik, 117, 219–249.
   Web of Science ®Google Scholar
• Emerson, J. W., and Kane, M. J. (2012), “Don’t Drown in the Data,” Significance, 9, 38–39.
   Google Scholar
• Enea, M. (2009), “Fitting Linear Models and Generalized Linear Models With Large Data Sets in R,” Conference on “Statistical Methods for the Analysis of Large Data-sets,” Italian Statistical Society, Chieti-Pescara, 2009, pp. 411–414.
   Google Scholar
• Fan, J., Han, F., and Liu, H. (2014), “Challenges of Big Data Analysis,” National Science Review, 1, 293–314.
   PubMed Web of Science ®Google Scholar
• Fernández, A., Río, S., López, V., Bawakid, A., Jesus, M., Ben´tez, J. M., and Herrera, F. (2014), “Big Data With Cloud Computing: An Insight on the Computing Environment, MapReduce, and Programming Frameworks,” WIREs Data Mining Knowledge Discovery, doi: 4, 380–409. doi:10.1002/widm.1134.
   Web of Science ®Google Scholar
• Guha, S., Hafen, R., Rounds, J., Xia, J., Li, J., Xi, B., and Cleveland, W. S. (2012), “Large Complex Data: Divide and Recombine (D&R) With Rhipe,” Stat, 1, 53–67.
   Google Scholar
• Hinkley, D. V., and Runger, G. (1984), “The Analysis of Transformed Data,” Journal of the American Statistical Association, 79, 302–309.
   Web of Science ®Google Scholar
• Ho, L. H., Feng, S. Y., and Yen, T. M. (2014), “Using Modified IPA to Improve Service Quality of Standard Hotel in Taiwan,” Journal of Service Science and Management, 7, 222–234.
   Google Scholar
• Hogg, R. V., McKean, J. W., and Craig, A. T. (2005), Introduction to Mathematical Statistics ( 6th ed.), Upper Saddle River, NJ: Pearson Prentice Hall Publisher.
   Google Scholar
• Hossain, M. Z. (2011), “The Use of Box-Cox Transformation Technique in Economic and Statistical Analyses,” Journal of Emerging Trends in Economics and Management Sciences, 1, 32–39.
   Google Scholar
• Hou, Q., Mahnken, J. D., Gajewski, B. J., and Dunton, N. (2011), “The Box-Cox Power Transformation on Nursing Sensitive Indicators: Does it Matter if Structural Effects are Omitted During the Estimation of the Transformation Parameters,” BMC Medical Research Methodology, 11, article number 118.
   PubMed Web of Science ®Google Scholar
• John, J. A., and Draper, N. R. (1980), “An Alternative Family of Transformation,” Applied Statistics, 29, 190–197.
   Google Scholar
• Karloff, H., Suri, S., and Vassilvitskii, S. (2010), “A Model of Computation for MapReduce,” in Proceeding of SODA’10 Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 938–948.
   Google Scholar
• Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005), Applied Linear Statistical Models, Boston MA: McGraw-Hill Irwin.
   Google Scholar
• Li, D., and Qiao, H. (2012), “A Positive Analysis of the National Debt Dependence Degree and Related Factors,” Communications in Information Science and Management Engineering, 2, 44–48.
   Google Scholar
• Lin, N., and Xi, R. (2011), “Aggregated Estimating Equation Estimation,” Statistics and Its Interface, 4, 73–83.
   Web of Science ®Google Scholar
• Ma, P., and Sun, X. (2015), “Leveraging for Big Data Regression,” WIREs Computational Statistics, 7, 70–76.
   Web of Science ®Google Scholar
• Maciejewski, R., Pattath, A., Ko, S., Hafen, R., Cleveland, W. S., and Ebert, D. S. (2013), “Automated Box-Cox Transformations for Improved Visual Encoding,” IEEE Transactions on Visualization and Computer Graphics, 19, 130–140.
   PubMed Web of Science ®Google Scholar
• Meeker, W. Q., and Hong, Y. (2014), “Reliability Meets Big Data: Opportunities and Challenges,” Qualify Engineering, 26, 102–116.
   Web of Science ®Google Scholar
• Miner, D., and Shook, A. (2012), MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, Sebastpool, CA: O’Reilly Media Inc.
   Google Scholar
• Murphy, S. A., and van der Vaart, A. W. (2000), “On Profile Likelihood,” Journal of the American Statistical Association, 95, 449–465.
   Web of Science ®Google Scholar
• Nelson, H. L., and Granger, C. W. J. (1979), “Experience With Using the Box-Cox Transformation With Forecasting Economic Time Series,” Journal of Econometrics, 10, 57–69.
   Google Scholar
• Osborne, J. W. (2010), “Improving Your Data Transformations: Applying the Box-Cox Transformation,” Practical Assessment, Research & Evaluation, 12, 1–9.
   Google Scholar
• Ovyn, S., Rouby, X., and Lemaitre, V. (2009), “DELPHES, a Framework for Fast Simulation of a Generic Collider Experiment,” arXiv:0903.2225.
   Google Scholar
• Patefield, W. M. (1977), “On the Maximized Likelihood Function,” Sankhyā, Series B, 39, 92–96.
   Google Scholar
• Peltier, M. R., Wilcox, C. J., and Sharp, D. C. (1998), “Technical Note: Application of the Box-Cox Data Transformation to Animal Science Experiments,” Journal of Animal Science, 76, 847–849.
   PubMed Web of Science ®Google Scholar
• Pericchi, L. R. (1981), “A Bayesian Approach to Transformations to Normality,” Biometrika, 68, 35–43.
   Web of Science ®Google Scholar
• Popo, J., Carrera, D., Becerra, Y., Steinder, M., and Whalley, I. (2010), “Performance-driven Task Co-scheduling for MapReduce Environments,” NOMS, pp. 374–380.
   Google Scholar
• Sjöstrand, T., Mrenna, S., and Skands, P. (2006), “PYTHIA 6.4 Physics and Manual,” Journal of High Energy Physics, article number 26
   Web of Science ®Google Scholar
• Sweeting, T. J. (1984), “On the Choice of the Prior Distribution for the Box-Cox Transformed Linear Model,” Biometrika, 71, 127–134.
   Web of Science ®Google Scholar
• Vitter, J. S. (2008), Algorithms and Data Structures for External Memory, Hanover, MA: Now Publication Inc.
   Google Scholar
• Wen, H., Bu, X., and Zhang, L. (2013), “The Application of Box-Cox Transformation in Selecting Functional Form for Hedonic Price Models,” Applied Mechanics and Materials, 360, 2869–2875.
   Google Scholar
• Zwiener, I., Frisch, B., and Binder, H. (2014), “Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures,” PLOS One, 9, article number e85150.
   Web of Science ®Google Scholar