New Frontiers of Biostatistics and Bioinformatics. 1st ed. 2018

種類:

電子ブック

責任表示:

edited by Yichuan Zhao, Ding-Geng Chen

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2018

著者名:

• Zhao, Yichuan.
• Chen, Ding-Geng.
• SpringerLink (Online service)

シリーズ名:

ICSA Book Series in Statistics ;

ISBN:

9783319993898 [3319993895]    

注記:

Chapter1. Importance of Adjusting for Multi-Stage Design when Analyzing Data from Complex Surveys -- Chapter2. A selective overview of semiparametric mixture of regression models -- Chapter3. Estimating the Confidence Interval of Evolutionary Stochastic Process Mean from Wavelet based Bootstrapping -- Chapter4. A New Wavelet-Based Approach for Mass Spectrometry Data Classification -- Chapter5. Identification of Pathway-Modulating Genes using the Biomedical Literature Mining -- Chapter6. Equivalence tests in subgroup analyses -- Chapter7. Empirical Study on High-Dimensional Variable Selection and Prediction under Competing Risks -- Chapter8. Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data -- Chapter9. Discriminant Analysis and Normalization Methods for Next-generation Sequencing Data -- Chapter10. Rank-based Empirical Likelihood for Regression Models with Responses Missing at Random -- Chapter11. Nonparametric Estimation of a Hazard Rate Function with Right Truncated Data -- Chapter1
This book is comprised of presentations delivered at the 5th Workshop on Biostatistics and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two selected papers from the workshop, this book showcases the most current advances in the field, presenting new methods, theories, and case applications at the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics and bioinformatics have been playing a key role in statistics and other scientific research fields in recent years. The goal of the 5th Workshop on Biostatistics and Bioinformatics was to stimulate research, foster interaction among researchers in field, and offer opportunities for learning and facilitating research collaborations in the era of big data. The resulting volume offers timely insights for researchers, students, and industry practitioners. .

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