Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process Poses new challenges and calls for scalable solutions to the analysis of such high dimensional data Present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data INDICE: Introduction.- Multiple comparisons.- marginal model vs. conditonal model.- multivariate nonparametric regression.- Multivariate ROC.- Methods for estimating prediction error.- Trees.- bagging/boosting/model averaging.- SVM and Sparse SVM.- Bayesian.- Appendix.
- ISBN: 978-0-387-69763-5
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 400
- Fecha Publicación: 01/10/2008
- Nº Volúmenes: 1
- Idioma: Inglés