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Statistical Application in Economics

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DOI: 10.4236/ojs.2012.21013    8,652 Downloads   14,244 Views   Citations
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ABSTRACT

Statisticians have recently proposed some methods for ranking the gene variables with outlier expressions. The major attraction of these methods is their ability to select the variables which show systematic decrease or increase in only a subset of samples in the disease group. In order to fully account for the outliers, in this article, we truncate the expression values and propose an alternative method to rank the variables with systematic increase or decrease. The proposed statistic is very simple to implement. Simulations and real data study show that the proposed statistic has a more powerful ability to rank the variables than some methods in literature.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

J. Luo, "Statistical Application in Economics," Open Journal of Statistics, Vol. 2 No. 1, 2012, pp. 120-123. doi: 10.4236/ojs.2012.21013.

References

[1] T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, M. Loh, J. Downing and M. Caligiuri, “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, Vol. 286, No. 5439, 1999, pp. 531-536. doi:10.1126/science.286.5439.531
[2] V. Tusher, R. Tibshirani and G. Chu, “Significance Analysis of Microarrays Applied to the Ionizing Radiation Response,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 98, No. 9, 2001, pp. 5116-5121. doi:10.1073/pnas.091062498
[3] J. Lyons-Weiler, S. Patel, M. Becich and T. Godfrey, “Tests for Finding Complex Patterns of Differential Expression in Cancers: Towards Individualized Medicine,” BMC Bioinformatics, Vol. 5, No. 110, 2004, pp. 1-9.
[4] D. Allison, X. Cui, G. Page and M. Sabripour, “Microarray Data Analysis: From Disarray to Consolidation and Consensus,” Nature Reviews Genetics, Vol. 7, No. 1, 2006, pp. 55-65. doi:10.1038/nrg1749
[5] M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida,R. Spang, H. Zuzan,J. Olson, J. Marks and J. Nevins, “Predicting the Clinical Status of Human Breast Cancer by Using Gene Expression Profiles,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 98, No. 20, 2001, pp. 11426-11467. doi:10.1073/pnas.201162998
[6] K. Rieger, W. Hong, V. Tusher, J. Tang, R. Tibshirani and G. Chu, “Toxicity From Radiation Therapy Associated with Abnormal Transcriptional Responses to DNA Damage,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 101, No. 17, 2004, pp. 6635-6640. doi:10.1073/pnas.0307761101
[7] D. Witten and R. Tibshirani, “A Comparison of Fold Change and The t-statistic for Microarray Data Analysis,” Technical Report, Stanford University, 2007.
[8] B. Efron, R. Tibshirani, J. Storey and V. Tusher, “Empirical Bayes Analysis of a Microarray Experiment,” Journal of the American Statistical Association, Vol. 96, No. 456, 2001, pp. 1151-1160.
[9] S. Dudoit, Y. Yang, M. Callow and T. Speed, “Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments,” Statistica Sinica, Vol. 12, No. 1, 2002, pp. 111-139.
[10] S. Tomlins, D. Rhodes, S. Perner, S. Dhanasekaran, R. Mehra, X. Sun, S. Varambally, X. Cao, J. Tchinda, R. Kuefer, et al., “Recurrent Fusion of Tmprss2 and ets Transcription Factor Genes in Prostate Cancer,” Science, Vol. 310, No. 5748, 2005, pp. 644-648. doi:10.1126/science.1117679
[11] R. Tibshirani and R. Hastie, “Outlier Sums for Differential Gene Expression Analysis,” Biostatistics, Vol. 8, No. 1, 2007, pp. 2-8. doi:10.1093/biostatistics/kxl005
[12] B. Wu, “Cancer Outlier Differential Gene Expression Detection,” Biostatistics, Vol. 8, No. 3, 2007, pp. 566-575. doi:10.1093/biostatistics/kxl029
[13] B. Efron and R. Tibshirani, “Empirical Bayes Methods and False Discovery Rates for Microarrays,” Genetic Epidemiology, Vol. 23, No. 1, 2002, pp. 70-86. doi:10.1002/gepi.1124
[14] K. Dobbin, J. Shih and R. Simon, “Questions and Answers on Design of Dual-label Microarrays for Identifying Differentially Expressed Genes,” Journal of National Cancer Institute, Vol. 95, No. 18, 2003, pp. 1362-1369. doi:10.1093/jnci/djg049
[15] S. Wang and J. Chen, “Sample Size for Identifying Differentially Expressed Genes in Microarray Experiments,” Journal of Computational Biology, Vol. 11, No. 4, 2004, pp. 714-726. doi:10.1089/cmb.2004.11.714
[16] Y. Pawitan, S. Michiels, S. Koscielny, A. Gusnanto and A. Ploner, “False Discovery Rate, Sensitivity and Sample Size for Microarray Studies,” Bioinformatics, Vol. 21, No. 13, 2005, pp. 3017-3024. doi:10.1093/bioinformatics/bti448

  
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