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CSDA
2011
7 years 7 months ago
Error rates for multivariate outlier detection
Multivariate outlier identification requires the choice of reliable cut-off points for the robust distances that measure the discrepancy from the fit provided by high-breakdown...
Andrea Cerioli, Alessio Farcomeni
GCB
2010
Springer
198views Biometrics» more  GCB 2010»
8 years 1 months ago
RNALfoldz: Efficient Prediction of Thermodynamically Stable, Local Secondary Structures
Abstract: The search for local RNA secondary structures and the annotation of unusually stable folding regions in genomic sequences are two well motivated bioinformatic problems. I...
Andreas R. Gruber, Stephan H. F. Bernhart, You Zho...
BMCBI
2005
153views more  BMCBI 2005»
8 years 3 months ago
A comparative review of estimates of the proportion unchanged genes and the false discovery rate
Background: In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by...
Per Broberg
BMCBI
2006
95views more  BMCBI 2006»
8 years 3 months ago
A simple method for assessing sample sizes in microarray experiments
In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments. Our method starts with the output from a permutation-based anal...
Robert Tibshirani
BMCBI
2006
187views more  BMCBI 2006»
8 years 3 months ago
Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false
Background: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads ...
Harvey J. Motulsky, Ronald E. Brown
CSSC
2008
110views more  CSSC 2008»
8 years 3 months ago
Bayesian False Discovery Rate Wavelet Shrinkage: Theory and Applications
The interest in inference in the wavelet domain remains vibrant area of statistical research because of needs of scientific community to process and explore massive data sets. Prim...
Ilya Lavrik, Yoon Young Jung, Fabrizio Ruggeri, Br...
BMCBI
2008
122views more  BMCBI 2008»
8 years 3 months ago
Determining gene expression on a single pair of microarrays
Background: In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently fe...
Robert W. Reid, Anthony A. Fodor
BMCBI
2008
99views more  BMCBI 2008»
8 years 3 months ago
Ranking analysis of F-statistics for microarray data
Background: Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, ide...
Yuan-De Tan, Myriam Fornage, Hongyan Xu
BMCBI
2007
121views more  BMCBI 2007»
8 years 3 months ago
A constrained polynomial regression procedure for estimating the local False Discovery Rate
Background: In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR), which...
Cyril Dalmasso, Avner Bar-Hen, Philippe Broët
BMCBI
2008
129views more  BMCBI 2008»
8 years 3 months ago
A unified approach to false discovery rate estimation
Background: False discovery rate (FDR) methods play an important role in analyzing highdimensional data. There are two types of FDR, tail area-based FDR and local FDR, as well as ...
Korbinian Strimmer
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