107 research outputs found
Measuring reproducibility of high-throughput experiments
Reproducibility is essential to reliable scientific discovery in
high-throughput experiments. In this work we propose a unified approach to
measure the reproducibility of findings identified from replicate experiments
and identify putative discoveries using reproducibility. Unlike the usual
scalar measures of reproducibility, our approach creates a curve, which
quantitatively assesses when the findings are no longer consistent across
replicates. Our curve is fitted by a copula mixture model, from which we derive
a quantitative reproducibility score, which we call the "irreproducible
discovery rate" (IDR) analogous to the FDR. This score can be computed at each
set of paired replicate ranks and permits the principled setting of thresholds
both for assessing reproducibility and combining replicates. Since our approach
permits an arbitrary scale for each replicate, it provides useful descriptive
measures in a wide variety of situations to be explored. We study the
performance of the algorithm using simulations and give a heuristic analysis of
its theoretical properties. We demonstrate the effectiveness of our method in a
ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Advances and Applications of DSmT for Information Fusion
This book is devoted to an emerging branch of Information Fusion based on new approach for modelling the fusion problematic when the information provided by the sources is both uncertain and (highly) conflicting. This approach, known in literature as DSmT (standing for Dezert-Smarandache Theory), proposes new useful rules of combinations
Multispace & Multistructure. Neutrosophic Transdisciplinarity (100 Collected Papers of Sciences), Vol. IV
The fourth volume, in my book series of “Collected Papers”, includes 100 published and unpublished articles, notes, (preliminary) drafts containing just ideas to be further investigated, scientific souvenirs, scientific blogs, project proposals, small experiments, solved and unsolved problems and conjectures, updated or alternative versions of previous papers, short or long humanistic essays, letters to the editors - all collected in the previous three decades (1980-2010) – but most of them are from the last decade (2000-2010), some of them being lost and found, yet others are extended, diversified, improved versions. This is an eclectic tome of 800 pages with papers in various fields of sciences, alphabetically listed, such as: astronomy, biology, calculus, chemistry, computer programming codification, economics and business and politics, education and administration, game theory, geometry, graph theory, information fusion, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, psychology, quantum physics, scientific research methods, and statistics. It was my preoccupation and collaboration as author, co-author, translator, or cotranslator, and editor with many scientists from around the world for long time. Many topics from this book are incipient and need to be expanded in future explorations
Stochastic Approximation and Newton's Estimate of a Mixing Distribution
Many statistical problems involve mixture models and the need for
computationally efficient methods to estimate the mixing distribution has
increased dramatically in recent years. Newton [Sankhya Ser. A 64 (2002)
306--322] proposed a fast recursive algorithm for estimating the mixing
distribution, which we study as a special case of stochastic approximation
(SA). We begin with a review of SA, some recent statistical applications, and
the theory necessary for analysis of a SA algorithm, which includes Lyapunov
functions and ODE stability theory. Then standard SA results are used to prove
consistency of Newton's estimate in the case of a finite mixture. We also
propose a modification of Newton's algorithm that allows for estimation of an
additional unknown parameter in the model, and prove its consistency.Comment: Published in at http://dx.doi.org/10.1214/08-STS265 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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