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THE PERFORMANCE OF SOME STATISTICAL PROCEDURES USED IN CASE-CONTROL STUDIES AND METHYLOMICS

Part of: Parametric inference

Published online by Cambridge University Press:  08 January 2020

RUPERT E. H. KUVEKE Open the ORCID record for RUPERT E. H. KUVEKE [Opens in a new window]
RUPERT E. H. KUVEKE*
Affiliation:
Department of Mathematics and Statistics, La Trobe University, Bundoora3086, Victoria, Australia email r.kuveke@latrobe.edu.au
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Abstract

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Keywords

post-model-selection confidence intervalcoverage probabilitygeneral regression modelDNA methylation analysis

MSC classification

Primary: 62F03: Hypothesis testing
Secondary: 92D20: Protein sequences, DNA sequences
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 101 , Issue 2 , April 2020 , pp. 342 - 344
Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to La Trobe University in March 2019; degree approved on 14 August 2019; principal supervisor Paul Kabaila, co-supervisor Agus Salim.

References

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Hurvich, C. M. and Tsai, C.-L., ‘The impact of model selection on inference in linear regression’, Amer. Statist. 44(3) (1990), 214–217.Google Scholar
Kabaila, P. and Kuveke, R. E. H., ‘The large sample coverage probability of confidence intervals in general regression models after a preliminary hypothesis test’, Scand. J. Stat. 46(2) (2019), 432–445.CrossRefGoogle Scholar
Korthauer, K. D., Chakraborty, S., Benjamini, Y. and Irizarry, R. A., ‘Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing’, Biostatistics 20(3) (2018), 367–383.CrossRefGoogle Scholar
Shafi, A., Mitrea, C., Nguyen, T. and Draghici, S., ‘A survey of the approaches for identifying differential methylation using bisulfite sequencing data’, Brief. Bioinform. 19(5) (2018), 737–753.CrossRefGoogle ScholarPubMed