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- ANCOVA; cross validation; efficiency augmentation; Mayo PBC data; semi-parametric efficiency (1)
- Amplifications (1)
- BLUPs; Kernel function; Model/variable selection; Nonparametric regression; Penalized likelihood; REML; Score test; Smoothing parameter; Support vector machines (1)
- Cancer (1)
- Clustered/longitudinal data; Generalized estimating equations; Generalized linear mixed models; Kernel method (1)
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- Copy number (1)
- DNA (1)
- Deletions (1)
- Diffusion tensor imaging; random matrix; likelihood ratio test; manifold-valued data; Satterthwaite approximation; multiple testing (1)
- Genetics (1)
- Genomic alterations (1)
- Intensity ratios (1)
- MCMC (1)
- Multiple testing; multiple comparisons; mixture models; Poisson regression; genome-wide association (1)
- Tumor (1)
Articles 1 - 9 of 9
Full-Text Articles in Statistical Methodology
Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data, Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer
Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data, Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer
Harvard University Biostatistics Working Paper Series
No abstract provided.
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d, Armin Schwartzman, Yulia Gavrilov, Robert J. Adler
Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d, Armin Schwartzman, Yulia Gavrilov, Robert J. Adler
Harvard University Biostatistics Working Paper Series
No abstract provided.
The Effect Of Correlation In False Discovery Rate Estimation, Armin Schwartzman, Xihong Lin
The Effect Of Correlation In False Discovery Rate Estimation, Armin Schwartzman, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Group Comparison Of Eigenvalues And Eigenvectors Of Diffusion Tensors, Armin Schwartzman, Robert F. Dougherty, Jonathan E. Taylor
Group Comparison Of Eigenvalues And Eigenvectors Of Diffusion Tensors, Armin Schwartzman, Robert F. Dougherty, Jonathan E. Taylor
Harvard University Biostatistics Working Paper Series
No abstract provided.
Empirical Null And False Discovery Rate Inference For Exponential Families, Armin Schwartzman
Empirical Null And False Discovery Rate Inference For Exponential Families, Armin Schwartzman
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Bayesian Hidden Markov Modeling Of Array Cgh Data, Subharup Guha, Yi Li, Donna Neuberg
Bayesian Hidden Markov Modeling Of Array Cgh Data, Subharup Guha, Yi Li, Donna Neuberg
Harvard University Biostatistics Working Paper Series
Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for …
Semiparametric Estimation In General Repeated Measures Problems, Xihong Lin, Raymond J. Carroll
Semiparametric Estimation In General Repeated Measures Problems, Xihong Lin, Raymond J. Carroll
Harvard University Biostatistics Working Paper Series
This paper considers a wide class of semiparametric problems with a parametric part for some covariate effects and repeated evaluations of a nonparametric function. Special cases in our approach include marginal models for longitudinal/clustered data, conditional logistic regression for matched case-control studies, multivariate measurement error models, generalized linear mixed models with a semiparametric component, and many others. We propose profile-kernel and backfitting estimation methods for these problems, derive their asymptotic distributions, and show that in likelihood problems the methods are semiparametric efficient. While generally not true, with our methods profiling and backfitting are asymptotically equivalent. We also consider pseudolikelihood methods …