Optimal Shrinkage Estimation of Variances With Applications to Microarray Data Analysis
暂无分享,去创建一个
[1] C. Stein. Inadmissibility of the usual estimator for the variance of a normal distribution with unknown mean , 1964 .
[2] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[3] Olivier Ledoit,et al. A well-conditioned estimator for large-dimensional covariance matrices , 2004 .
[4] Pierre R. Bushel,et al. STATISTICAL ANALYSIS OF A GENE EXPRESSION MICROARRAY EXPERIMENT WITH REPLICATION , 2002 .
[5] Karl J. Friston,et al. Variance Components , 2003 .
[6] Lawrence D. Brown,et al. INADMISSIBILITY OF THE USUAL ESTIMATORS OF SCALE PARAMETERS IN PROBLEMS WITH UNKNOWN LOCATION AND SCALE PARAMETERS , 1968 .
[7] Pierre Baldi,et al. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..
[8] T. Ferguson. A Course in Large Sample Theory , 1996 .
[9] D. Cavalieri,et al. Fundamentals of cDNA microarray data analysis. , 2003, Trends in genetics : TIG.
[10] Ingrid Lönnstedt. Replicated microarray data , 2001 .
[11] J. F. Brewster,et al. Improving on Equivariant Estimators , 1974 .
[12] Raymond J Carroll,et al. DNA Microarray Experiments: Biological and Technological Aspects , 2002, Biometrics.
[13] John D. Storey,et al. SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays , 2003 .
[14] R. Littell. SAS System for Mixed Models , 1996 .
[15] Hao Wu,et al. MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments , 2003 .
[16] George Casella,et al. Developments in Decision-Theoretic Variance Estimation , 1990 .
[17] Irene A. Stegun,et al. Handbook of Mathematical Functions. , 1966 .
[18] Jae K. Lee,et al. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays , 2003, Bioinform..
[19] Alexander Kamb,et al. A simple method for statistical analysis of intensity differences in microarray-derived gene expression data , 2001, BMC biotechnology.
[20] Richard Simon,et al. A random variance model for detection of differential gene expression in small microarray experiments , 2003, Bioinform..
[21] M. Ghosh,et al. INADMISSIBILITY OF THE BEST EQUIVARIANT ESTIMATORS OF THE VARIANCE-COVARIANCE MATRIX, THE PRECISION MATRIX, AND THE GENERALIZED VARIANCE UNDER ENTROPY LOSS , 1987 .
[22] François Perron. Equivariant estimators of the covariance matrix , 1990 .
[23] X. Cui,et al. Improved statistical tests for differential gene expression by shrinking variance components estimates. , 2005, Biostatistics.
[24] T. Kubokawa. A Unified Approach to Improving Equivariant Estimators , 1994 .
[25] Tatsuya Kubokawa,et al. Estimating the covariance matrix: a new approach , 2003 .
[26] Tatsuya Kubokawa,et al. Shrinkage and modification techniques in estimation of variance and the related problems : A review , 1998 .
[27] X. Cui,et al. Statistical tests for differential expression in cDNA microarray experiments , 2003, Genome Biology.
[28] Olivier Ledoit,et al. Honey, I Shrunk the Sample Covariance Matrix , 2003 .
[29] C. Stein,et al. Estimation with Quadratic Loss , 1992 .
[30] C M Kendziorski,et al. On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles , 2003, Statistics in medicine.
[31] Brian S. Yandell,et al. Adaptive Gene Picking with Microarray Data: Detecting Important Low Abundance Signals , 2003 .
[32] S. Dudoit,et al. Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. , 2000, Genome research.