Výsledky vyhledávání - "Omkar Muralidharan"

Empirical Bayes Mean Estimation With Nonparametric Errors Via Order Statistic Regression on Replicated Data

Autor: Omkar Muralidharan, Dennis L. Sun, Nikolaos Ignatiadis, Sujayam Saha

Publikováno v: Journal of the American Statistical Association. :1-13

We study empirical Bayes estimation of the effect sizes of N units from K noisy observations on each unit. We show that it is possible to achieve near-Bayes optimal mean squared error, without any assumptions or knowledge about the effect size distri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::033daa3e64ff2c9aa9d47d4315b78558
https://doi.org/10.1080/01621459.2021.1967164

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Teaching Statistics at Google-Scale

Autor: Stefan Wager, Omkar Muralidharan, Nicholas Chamandy

Publikováno v: The American Statistician. 69:283-291

Modern data and applications pose very different challenges from those of the 1950s or even the 1980s. Students contemplating a career in statistics or data science need to have the tools to tackle problems involving massive, heavy-tailed data, often

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::38939e6ed799e8df124fe9f5f41309e3
https://doi.org/10.1080/00031305.2015.1089790

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Detecting mutations in mixed sample sequencing data using empirical Bayes

Autor: Hanlee P. Ji, Nan Zhang, Omkar Muralidharan, John Bell, Georges Natsoulis

Publikováno v: Ann. Appl. Stat. 6, no. 3 (2012), 1047-1067

We develop statistically based methods to detect single nucleotide DNA mutations in next generation sequencing data. Sequencing generates counts of the number of times each base was observed at hundreds of thousands to billions of genome positions in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::695f8d4db3ceb9a0396bc78ed77d660e
http://arxiv.org/abs/1209.6453

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A cross-sample statistical model for SNP detection in short-read sequencing data

Autor: John Bell, Nan Zhang, Georges Natsoulis, Itai Kela, Omkar Muralidharan, Hua Xu, Daniel E. Newburger, Hanlee P. Ji

Publikováno v: Nucleic Acids Research

Highly multiplex DNA sequencers have greatly expanded our ability to survey human genomes for previously unknown single nucleotide polymorphisms (SNPs). However, sequencing and mapping errors, though rare, contribute substantially to the number of fa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edfed102ec8946644e825227b2169456
https://pubmed.ncbi.nlm.nih.gov/22064853

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Detecting column dependence when rows are correlated and estimating the strength of the row correlation

Autor: Omkar Muralidharan

Publikováno v: Electron. J. Statist. 4 (2010), 1527-1546

Microarray experiments often yield a normal data matrix X whose rows correspond to genes and columns to samples. We commonly calculate test statistics Z=Xw, where Zi is a test statistic for the ith gene, and apply false discovery rate (FDR) controlli

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https://projecteuclid.org/euclid.ejs/1293113417

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An empirical Bayes mixture method for effect size and false discovery rate estimation

Autor: Omkar Muralidharan

Publikováno v: Ann. Appl. Stat. 4, no. 1 (2010), 422-438

Many statistical problems involve data from thousands of parallel cases. Each case has some associated effect size, and most cases will have no effect. It is often important to estimate the effect size and the local or tail-area false discovery rate

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Ultrasensitive detection of rare mutations using next-generation targeted resequencing

Autor: Sheldon T. Brown, Mark Holodniy, Hanlee P. Ji, Omkar Muralidharan, Jason D. Buenrostro, Nan Zhang, Patrick Flaherty, Georges Natsoulis, Mark A. Winters, John Bell

Publikováno v: Nucleic Acids Research

With next-generation DNA sequencing technologies, one can interrogate a specific genomic region of interest at very high depth of coverage and identify less prevalent, rare mutations in heterogeneous clinical samples. However, the mutation detection

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d6940bc952906827436fdfef411bc68
http://europepmc.org/articles/PMC3245950

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