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Full-Text Articles in Physical Sciences and Mathematics
Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng
Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng
U.C. Berkeley Division of Biostatistics Working Paper Series
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable …
Design Considerations For Efficient And Effective Microarray Studies, M. Kathleen Kerr
Design Considerations For Efficient And Effective Microarray Studies, M. Kathleen Kerr
UW Biostatistics Working Paper Series
This paper describes the theoretical and practical issues in experimental design for gene expression microarrays. Specifically, this paper (1) discusses the basic principles of design (randomization, replication, and blocking) as they pertain to microarrays, and (2) provides some general guidelines for statisticians designing microarray studies.
A Semiparametric Regression Model For Oligonucleotide Arrays, Jianhua Hu, Guosheng Yin
A Semiparametric Regression Model For Oligonucleotide Arrays, Jianhua Hu, Guosheng Yin
Journal of Modern Applied Statistical Methods
A semiparametric model incorporating the spline smoothing technique is proposed to study oligonucleotide gene expression data. No specific parametric functional form is assumed for mismatch probe intensities, which allows much more flexibility in the fitted model. The new approach improves the model fitting, hence the estimation of expression indexes. The method is applied to a data set of 18 HuGeneFL arrays.