Omnibus testing and gene filtration in microarray data analysis

Journal of Applied Statistics

Volumn 35, Issue 1, 2008, Pages 31-47

  Dai, Hongying ^a   Charnigo, Richard ^b  

^a Columbus State University   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

Author keywords

Beta contamination model; D test; MLRT; MMLEs; Multiple comparisons; P values

Indexed keywords

EID: 38049098083     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664760701683528     Document Type: Article

References (29)

1. Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory. pp. 267-281. Akademiai Kiado, Budapest
2. Allison, D. B. (2002) A mixture model approach for the analysis of microarray gene expression data. Comput. Statist. and Data Anal., 39, pp. 1-20.
3. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B, 57, pp. 289-300.
4. Benjamini, Y. and Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. Ann. Stat., 29, pp. 1165-1188.
5. Blalock, E. M. (2003) Gene microarrays in hippocampal aging: Statistical profiling identifies novel processes correlated with cognitive impairment. J. Neurosci., 23, pp. 3807-3819.
6. Burnham, K. P. and Anderson, D. R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer-Verlag, New York
7. Chakravarti, I., Laha, R. and Roy, J. (1967) Handbook of Methods of Applied Statistics, I. pp. 392-394. Wiley, Indianapolis
8. 2 distance between competing models. J. Am. Stat. Assoc, 99, pp. 488-498.
9. Chen, H. and Chen, J. (2001) The likelihood ratio test for homogeneity in the finite mixture models. Can. J. Statist., 29, pp. 201-215.
10. Chen, J. and Kalbfleisch, J. D. (2004) Modified likelihood ratio test in finite mixture models with a structural parameter. J. Stat. Plann. Inference, 129, pp. 93-107.
11. Chen, H., Chen, J. and Kalbfleisch, J. D. (2001) A modified likelihood ratio test for homogeneity in finite mixture models. J. R. Stat. Soc. Ser. B, 63, pp. 19-29.
12. Dacunha-Castelle, D. and Gassiat, E. (1999) Testing the order of a model using locally conic parameterization: Population mixtures and stationary ARMA processes. Ann. Stat., 27, pp. 1178-1209.
13. Dai, H. and Charnigo, R. (2007) Outlier detection and inferences in contaminated regression models.
14. Donahue, R. (1999) A note on information seldom reported via the p value. Amer. Stat., 53, pp. 303-306.
15. Eadie, W. (1982) Statistical Methods in Experimental Physics. Sec. Reprint, North-Holland
16. Genovese, C. and Wasserman, L. (2002) Operating characteristics and extensions of the false discovery rate procedure. J. R. Stat. Soc. Ser. B, 64, pp. 499-517.
17. Hartigan, J. A. (1985) A failure of likelihood asymptotics for normal mixtures. Proceedings of Berk Conference in Honor of J. Neyman and J. Kiefer Wadsworth, Belmont, CApp. 807-810.
18. Hwang, D. (2002) Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics, 18, pp. 1184-1193.
19. Lahiri, P. (2001) ModelSelection, IMS NSF-CBMS Regional Conference Series. Hayward
20. Lee, M. and Whitmore, G. (2002) Power and sample size for DNA microarray studies. Stat. Med., 21, pp. 3543-3570.
21. Lemdani, M. and Pons, O. (1999) Likelihood ratio tests in contamination models. Bernoulli, 5, pp. 705-719.
22. Lindsay, B. G. (1995) Mixture Models: Theory, Geometry and Applications. Institute of Mathematical Statistics, Hayward
23. Pan, W., Lin, J. and Le, C. (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biology, 3
24. Peng, X. J. (2003) Simultaneous inference and sample size considerations in microarray data analysis. University of Kentucky, Lexington
25. Schwarz, G. (1978) Estimating the dimension of a model. Ann. Stat., 6, pp. 461-464.
26. Shaffer, J. (1995) Multiple hypothesis testing: A review. Ann. Rev. Psychol., 46, pp. 561-584.
27. Stephens, M. (1974) EDF statistics for goodness of fit and some comparisons. J. Am. Stat. Assoc., 69, pp. 730-737.
28. Storey, J. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Ann. Stat., 31, pp. 2013-2035.
29. Zhu, H. and Zhang, H. (2004) Hypothesis testing in mixture regression models. J. R. Stat. Soc. Ser. B, 66, pp. 3-16.