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Full-Text Articles in Physical Sciences and Mathematics
Estimation Of A Non-Parametric Variable Importance Measure Of A Continuous Exposure, Chambaz Antoine, Pierre Neuvial, Mark J. Van Der Laan
Estimation Of A Non-Parametric Variable Importance Measure Of A Continuous Exposure, Chambaz Antoine, Pierre Neuvial, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We define a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders. The exposure features a reference level x0 with positive mass and a continuum of other levels. For the purpose of estimating it, we fully develop the semi-parametric estimation methodology called targeted minimum loss estimation methodology (TMLE) [van der Laan & Rubin, 2006; van der Laan & Rose, 2011]. We cover the whole spectrum of its theoretical study (convergence of the iterative procedure which is at the core of the TMLE methodology; consistency and asymptotic normality of the estimator), practical implementation, simulation …
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.
A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi
A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi
COBRA Preprint Series
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …
A Bayesian Model Averaging Approach For Observational Gene Expression Studies, Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg
A Bayesian Model Averaging Approach For Observational Gene Expression Studies, Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg
COBRA Preprint Series
Identifying differentially expressed (DE) genes associated with a sample characteristic is the primary objective of many microarray studies. As more and more studies are carried out with observational rather than well controlled experimental samples, it becomes important to evaluate and properly control the impact of sample heterogeneity on DE gene finding. Typical methods for identifying DE genes require ranking all the genes according to a pre-selected statistic based on a single model for two or more group comparisons, with or without adjustment for other covariates. Such single model approaches unavoidably result in model misspecification, which can lead to increased error …
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Johns Hopkins University, Dept. of Biostatistics Working Papers
Biomedical signals can arise from one or many sources including heart ,brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal are like electroencephalogram(EEG),electrocardiogram(ECG),etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The diagnosis of patient is based on visual observation of recorded ECG,EEG,etc, may not be accurate. To achieve better understanding , PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals . The immense scope in the field of biomedical-signal processing Independent Component Analysis( …
A Generalized Approach For Testing The Association Of A Set Of Predictors With An Outcome: A Gene Based Test, Benjamin A. Goldstein, Alan E. Hubbard, Lisa F. Barcellos
A Generalized Approach For Testing The Association Of A Set Of Predictors With An Outcome: A Gene Based Test, Benjamin A. Goldstein, Alan E. Hubbard, Lisa F. Barcellos
U.C. Berkeley Division of Biostatistics Working Paper Series
In many analyses, one has data on one level but desires to draw inference on another level. For example, in genetic association studies, one observes units of DNA referred to as SNPs, but wants to determine whether genes that are comprised of SNPs are associated with disease. While there are some available approaches for addressing this issue, they usually involve making parametric assumptions and are not easily generalizable. A statistical test is proposed for testing the association of a set of variables with an outcome of interest. No assumptions are made about the functional form relating the variables to the …