2,237 research outputs found
Robust compositional data analysis
Many practical data sets contain outliers or other forms of data inhomogeneities. Robust
statistics offers concepts how to deal with these situations where the data do not follow strict
model assumptions. These concepts are designed for the usual Euclidean space, and they can be
easily applied to compositional data if they are represented in this space as well. It turns out
that the isometric logratio (ilr) transformation is best suitable in the context of robust estimation.
Depending on the method applied, an interpretation of result is usually done in a back-transformed
space
Analysis of compositional data using robust methods. The R-package robCompositons
The free and open-source programming language and software environment R (R Development Core
Team, 2010) is currently both, the most widely used and most popular software for statistics and
data analysis. In addition, R becomes quite popular as a (programming) language, ranked currently
(February 2011) on place 25 at the TIOBE Programming Community Index (e.g., Matlab: 29, SAS:
30, see http://www.tiobe.com).
The basic R environment can be downloaded from the comprehensive R archive network (http://cran.rproject.org). R is enhanceable via packages which consist of code and structured standard documentation including code application examples and possible further documents (so called vignettes) showing
further applications of the packages.
Two contributed packages for compositional data analysis comes with R, version 2.12.1.: the package compositions (van den Boogaart et al., 2010) and the package robCompositions (Templ et al.,
2011).
Package compositions provides functions for the consistent analysis of compositional data and
positive numbers in the way proposed originally by John Aitchison (see van den Boogaart et al., 2010).
In addition to the basic functionality and estimation procedures in package compositions, package robCompositions provides tools for a (classical) and robust multivariate statistical analysis of
compositional data together with corresponding graphical tools. In addition, several data sets are
provided as well as useful utility functions
Classical and robust imputation of missing values for compositional data using balances
Classical and Robust Imputation of Missing Values for Compositional Data using Balance
Simplicial principal component analysis for density functions in Bayes spaces
Probability density functions are frequently used to characterize the distributional properties
of large-scale database systems. As functional compositions, densities primarily carry
relative information. As such, standard methods of functional data analysis (FDA) are not
appropriate for their statistical processing. The specific features of density functions are
accounted for in Bayes spaces, which result from the generalization to the infinite dimensional
setting of the Aitchison geometry for compositional data. The aim is to build up a
concise methodology for functional principal component analysis of densities. A simplicial
functional principal component analysis (SFPCA) is proposed, based on the geometry
of the Bayes space B2 of functional compositions. SFPCA is performed by exploiting the
centred log-ratio transform, an isometric isomorphism between B2 and L2 which enables
one to resort to standard FDA tools. The advantages of the proposed approach with respect
to existing techniques are demonstrated using simulated data and a real-world example of
population pyramids in Upper Austria
Photo Thermal Effect Graphene Detector Featuring 105 Gbit s-1 NRZ and 120 Gbit s-1 PAM4 Direct Detection
The challenge of next generation datacom and telecom communication is to
increase the available bandwidth while reducing the size, cost and power
consumption of photonic integrated circuits. Silicon (Si) photonics has emerged
as a viable solution to reach these objectives. Graphene, a single-atom thick
layer of carbon5, has been recently proposed to be integrated with Si photonics
because of its very high mobility, fast carrier dynamics and ultra-broadband
optical properties. Here, we focus on graphene photodetectors for high speed
datacom and telecom applications. High speed graphene photodetectors have been
demonstrated so far, however the most are based on the photo-bolometric (PB) or
photo-conductive (PC) effect. These devices are characterized by large dark
current, in the order of milli-Amperes , which is an impairment in
photo-receivers design, Photo-thermo-electric (PTE) effect has been identified
as an alternative phenomenon for light detection. The main advantages of
PTE-based photodetectors are the optical power to voltage conversion, zero-bias
operation and ultra-fast response. Graphene PTE-based photodetectors have been
reported in literature, however high-speed optical signal detection has not
been shown. Here, we report on an optimized graphene PTE-based photodetector
with flat frequency response up to 65 GHz. Thanks to the optimized design we
demonstrate a system test leading to direct detection of 105 Gbit s-1
non-return to zero (NRZ) and 120 Gbit s-1 4-level pulse amplitude modulation
(PAM) optical signal
Classical and robust regression analysis with compositional data
Compositional data carry their relevant information in the relationships (logratios) between the compositional parts. It is shown how this source of information can be used in regression modeling, where the composition could either form the response, or the explanatory part, or even both. An essential step to set up a regression model is the way how the composition(s) enter the model. Here, balance coordinates will be constructed that support an interpretation of the regression coefficients and allow for testing hypotheses of subcompositional independence. Both classical least-squares regression and robust MM regression are treated, and they are compared within different regression models at a real data set from a geochemical mapping project
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Search for CP violating top quark couplings in pp collisions at √s = 13 TeV
A preprint version of the article is available at arXiv:2205.07434v2 [hep-ex], https://arxiv.org/abs/2205.07434. It has not been certified by peer review.Results are presented from a search for CP violation in top quark pair production, using proton-proton collisions at a center-of-mass energy of 13 TeV. The data used for this analysis consist of final states with two charged leptons collected by the CMS experiment, and correspond to an integrated luminosity of 35.9 fb−1. The search uses two observables, O1 and O3, which are Lorentz scalars. The observable O1 is constructed from the four-momenta of the charged leptons and the reconstructed top quarks, while O3 consists of the four-momenta of the charged leptons and the b quarks originating from the top quarks. Asymmetries in these observables are sensitive to CP violation, and their measurement is used to determine the chromoelectric dipole moment of the top quark. The results are consistent with the expectation from the standard model. [Figure not available: see fulltext.].SCOAP
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Search for new Higgs bosons via same-sign top quark pair production in association with a jet in proton-proton collisions at s = 13 TeV
A search is presented for new Higgs bosons in proton-proton (pp) collision events in which a same-sign top quark pair is produced in association with a jet, via the pp→tH/A→ttc‾ and pp→tH/A→ttu‾ processes. Here, H and A represent the extra scalar and pseudoscalar boson, respectively, of the second Higgs doublet in the generalized two-Higgs-doublet model (g2HDM). The search is based on pp collision data collected at a center-of-mass energy of 13 TeV with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb−1. Final states with a same-sign lepton pair in association with jets and missing transverse momentum are considered. New Higgs bosons in the 200–1000 GeV mass range and new Yukawa couplings between 0.1 and 1.0 are targeted in the search, for scenarios in which either H or A appear alone, or in which they coexist and interfere. No significant excess above the standard model prediction is observed. Exclusion limits are derived in the context of the g2HDM
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