96 research outputs found
Non-Gaussian component analysis: testing the dimension of the signal subspace
Dimension reduction is a common strategy in multivariate data analysis which
seeks a subspace which contains all interesting features needed for the
subsequent analysis. Non-Gaussian component analysis attempts for this purpose
to divide the data into a non-Gaussian part, the signal, and a Gaussian part,
the noise. We will show that the simultaneous use of two scatter functionals
can be used for this purpose and suggest a bootstrap test to test the dimension
of the non-Gaussian subspace. Sequential application of the test can then for
example be used to estimate the signal dimension
Sliced average variance estimation for multivariate time series
Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response y and the predictors . Recently a time series version of sliced inverse regression, TSIR, was suggested, which applies approximate joint diagonalization of several supervised lagged covariance matrices to consider the temporal nature of the data. In this paper, we develop this concept further and propose a time series version of sliced average variance estimation, TSAVE. As both TSIR and TSAVE have their own advantages and disadvantages, we consider furthermore a hybrid version of TSIR and TSAVE. Based on examples and simulations we demonstrate and evaluate the differences between the three methods and show also that they are superior to apply their iid counterparts to when also using lagged values of the explaining variables as predictors
Extracting Conditionally Heteroskedastic Components using Independent Component Analysis
In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coefficient. As it is often of interest to identify all those components which exhibit stochastic volatility features we suggest a test statistic for this problem. We also show that a slightly modified version of the principal volatility component analysis can be seen as an ICA method. Finally, we apply the estimators in analysing a data set which consists of time series of exchange rates of seven currencies to US dollar. Supporting information including proofs of the theorems is available online
Salinity control on Na incorporation into calcite tests of the planktonic foraminifera Trilobatus sacculifer – Evidence from culture experiments and surface sediments
The quantitative reconstruction of past seawater salinity has yet to be achieved and the search for a direct and independent salinity proxy is ongoing. Recent culture and field studies show a significant positive correlation of Na/Ca with salinity in benthic and planktonic foraminiferal calcite. For accurate paleoceanographic reconstructions, consistent and reliable calibrations are necessary, which are still missing. In order to assess the reliability of foraminiferal Na/Ca as a direct proxy for seawater salinity, this study presents electron microprobe Na/Ca data, measured on cultured specimens of Trilobatus sacculifer. The culture experiments were conducted over a wide salinity range of 26 to 45, while temperature was kept constant. To further understand potential controlling factors of Na incorporation, measurements were also performed on foraminifera cultured at various temperatures in the range of 19.5 °C to 29.5 °C under constant salinity conditions. Foraminiferal Na/Ca ratios positively correlate with seawater salinity (Na/Caforam = 0.97 + 0.115 ⋅ Salinity, R = 0.97, p < 0.005). Temperature on the other hand exhibits no statistically significant relationship with Na/Ca ratios indicating salinity to be the dominant factor controlling Na incorporation. The culturing results are corroborated by measurements on T. sacculifer from Caribbean and Gulf of Guinea surface sediments. In conclusion, planktonic foraminiferal Na/Ca can be applied as a reliable proxy for reconstructing sea surface salinities, albeit species-specific calibrations might be necessary
Subgroup detection in genotype data using invariant coordinate selection
Background: The current gold standard in dimension reduction methods for high-throughput genotype data is the Principle Component Analysis (PCA). The presence of PCA is so dominant, that other methods usually cannot be found in the analyst's toolbox and hence are only rarely applied.Results: We present a modern dimension reduction method called 'Invariant Coordinate Selection' (ICS) and its application to high-throughput genotype data. The more commonly known Independent Component Analysis (ICA) is in this framework just a special case of ICS. We use ICS on both, a simulated and a real dataset to demonstrate first some deficiencies of PCA and how ICS is capable to recover the correct subgroups within the simulated data. Second, we apply the ICS method on a chicken dataset and also detect there two subgroups. These subgroups are then further investigated with respect to their genotype to provide further evidence of the biological relevance of the detected subgroup division. Further, we compare the performance of ICS also to five other popular dimension reduction methods.Conclusion: The ICS method was able to detect subgroups in data where the PCA fails to detect anything. Hence, we promote the application of ICS to high-throughput genotype data in addition to the established PCA. Especially in statistical programming environments like e.g. R, its application does not add any computational burden to the analysis pipeline
On the number of signals in multivariate time series
We assume a second-order source separation model where the observed
multivariate time series is a linear mixture of latent, temporally uncorrelated
time series with some components pure white noise. To avoid the modelling of
noise, we extract the non-noise latent components using some standard method,
allowing the modelling of the extracted univariate time series individually. An
important question is the determination of which of the latent components are
of interest in modelling and which can be considered as noise. Bootstrap-based
methods have recently been used in determining the latent dimension in various
methods of unsupervised and supervised dimension reduction and we propose a set
of similar estimation strategies for second-order stationary time series.
Simulation studies and a sound wave example are used to show the method's
effectiveness
The KELT Follow-Up Network And Transit False-Positive Catalog: Pre-Vetted False Positives For TESS
The Kilodegree Extremely Little Telescope (KELT) project has been conducting a photometric survey of transiting planets orbiting bright stars for over 10 years. The KELT images have a pixel scale of ~23\u27\u27 pixel⁻¹—very similar to that of NASA\u27s Transiting Exoplanet Survey Satellite (TESS)—as well as a large point-spread function, and the KELT reduction pipeline uses a weighted photometric aperture with radius 3\u27. At this angular scale, multiple stars are typically blended in the photometric apertures. In order to identify false positives and confirm transiting exoplanets, we have assembled a follow-up network (KELT-FUN) to conduct imaging with spatial resolution, cadence, and photometric precision higher than the KELT telescopes, as well as spectroscopic observations of the candidate host stars. The KELT-FUN team has followed-up over 1600 planet candidates since 2011, resulting in more than 20 planet discoveries. Excluding ~450 false alarms of non-astrophysical origin (i.e., instrumental noise or systematics), we present an all-sky catalog of the 1128 bright stars (6 \u3c V \u3c 13) that show transit-like features in the KELT light curves, but which were subsequently determined to be astrophysical false positives (FPs) after photometric and/or spectroscopic follow-up observations. The KELT-FUN team continues to pursue KELT and other planet candidates and will eventually follow up certain classes of TESS candidates. The KELT FP catalog will help minimize the duplication of follow-up observations by current and future transit surveys such as TESS
Developing an e-learning course on the use of PRO measures in oncological practice: health care professionals' preferences for learning content and methods
PURPOSE: Implementation of patient-reported outcome measures (PROMs) in clinical routine requires knowledge and competences regarding their use. In order to facilitate implementation, an e-learning course for health care professionals (HCPs) on the utilisation of European Organisation for Research and Treatment of Cancer (EORTC) PROMs in oncological clinical practice is being developed. This study aimed to explore future users' educational needs regarding content and learning methods. METHODS: The sequential mixed methods approach was applied. A scoping literature review informed the guideline for qualitative interviews with HCPs with diverse professional backgrounds in oncology and cancer advocates recruited using a purposive sampling strategy. An international online survey was conducted to validate the qualitative findings. RESULTS: Between December 2019 and May 2020, 73 interviews were conducted in 9 countries resulting in 8 topic areas (Basic information on PROs in clinical routine, Benefits of PRO assessments in clinical practice, Implementation of PRO assessments in clinical routine, Setup of PRO assessments for clinical application, Interpretation of PRO data, Integration of PROs into the communication with patients, Use of PROs in clinical practice, Self-management recommendations for patients based on PROs) subsequently presented in the online survey. The online survey (open between 3 June and 19 July 2020) was completed by 233 HCPs from 33 countries. The highest preference was indicated for content on interpretation of PRO data (97%), clinical benefits of assessing PRO data (95.3%) and implementation of routine PRO data assessment (94.8%). Regarding learning methods, participants indicated a high preference for practical examples that use a mixed approach of presentation (written, audio, video and interactive). CONCLUSION: Educational needs for an integration of PROs in communication in clinical care and coherent implementation strategies became evident. These results inform the development of an e-learning course to support HCPs in the clinical use of EORTC PRO measures
The Association of Antarctic Krill Euphausia superba with the Under-Ice Habitat
The association of Antarctic krill Euphausia superba with the under-ice habitat was investigated in the Lazarev Sea (Southern Ocean) during austral summer, autumn and winter. Data were obtained using novel Surface and Under Ice Trawls (SUIT), which sampled the 0–2 m surface layer both under sea ice and in open water. Average surface layer densities ranged between 0.8 individuals m−2 in summer and autumn, and 2.7 individuals m−2 in winter. In summer, under-ice densities of Antarctic krill were significantly higher than in open waters. In autumn, the opposite pattern was observed. Under winter sea ice, densities were often low, but repeatedly far exceeded summer and autumn maxima. Statistical models showed that during summer high densities of Antarctic krill in the 0–2 m layer were associated with high ice coverage and shallow mixed layer depths, among other factors. In autumn and winter, density was related to hydrographical parameters. Average under-ice densities from the 0–2 m layer were higher than corresponding values from the 0–200 m layer collected with Rectangular Midwater Trawls (RMT) in summer. In winter, under-ice densities far surpassed maximum 0–200 m densities on several occasions. This indicates that the importance of the ice-water interface layer may be under-estimated by the pelagic nets and sonars commonly used to estimate the population size of Antarctic krill for management purposes, due to their limited ability to sample this habitat. Our results provide evidence for an almost year-round association of Antarctic krill with the under-ice habitat, hundreds of kilometres into the ice-covered area of the Lazarev Sea. Local concentrations of postlarval Antarctic krill under winter sea ice suggest that sea ice biota are important for their winter survival. These findings emphasise the susceptibility of an ecological key species to changing sea ice habitats, suggesting potential ramifications on Antarctic ecosystems induced by climate change
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