Related papers: Principal Component Analysis of computed emission …
Studies on the structure of the photon are presented by means of the extraction of a leading order effective parton distribution in the photon and measurements of inclusive jet differential cross sections in photoproduction. Measurements of…
Principal component analysis (PCA) is routinely used in population genetics to assess genetic structure. With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, contemporary studies…
Using the Yguazu-a three-dimensional hydrodynamic code, we have computed a set of numerical simulations of heavy, supersonic, radiatively cooling jets including variabilities in both the ejection direction (precession) and the jet velocity…
In this work, we develop a novel principal component analysis (PCA) for semimartingales by introducing a suitable spectral analysis for the quadratic variation operator. Motivated by high-dimensional complex systems typically found in…
In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at $p$ locations and $n$ time points with the possibility that $p>n$. While principal component analysis…
Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed…
Studies on the structure of the photon are presented by means of the extraction of a leading order effective parton distribution in the photon and measurements of inclusive jet differential cross sections in photoproduction. Measurements of…
Modal analysis techniques are used to identify patterns and develop reduced-order models in a variety of fluid applications. However, experimentally acquired flow fields may be corrupted with incorrect and missing entries, which may degrade…
Principal Component analysis (PCA) is a useful statistical technique that is commonly used for multivariate analysis of correlated variables. It is usually applied as a dimension reduction method: the top principal components (PCs)…
Phylogenetic analysis of DNA or other data commonly gives rise to a collection or sample of inferred evolutionary trees. Principal Components Analysis (PCA) cannot be applied directly to collections of trees since the space of evolutionary…
We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results establishing the necessary and sufficient conditions for…
CONTEXT: In the interpretation of stellar jet observations, the physical parameters are usually determined from emission line ratios, obtained from spectroscopic observations or using the information contained in narrow band images. The…
We investigate general aspects of molecular line formation under conditions which are typical of prestellar cores. Focusing on simple linear molecules, we study formation of their rotational lines by radiative transfer simulations. We…
Modelling of deep VLA images of the jets in FR I radio galaxies has allowed us to derive their three-dimensional distributions of velocity, emissivity and magnetic-field structure on kiloparsec scales. By combining our models of jet…
We investigate the effect of star formation on turbulence in the Orion A and Ophiuchus clouds using principal component analysis (PCA). We measure the properties of turbulence by applying PCA on the spectral maps in $^{13}$CO, C$^{18}$O,…
Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return…
We investigate numerically and semi-analytically the collapse of low-mass, rotating prestellar cores. Initially, the cores are in approximate equilibrium with low rotation (the initial ratio of thermal to gravitational energy is $\alpha_0…
We explore the photospheric emission from a relativistic jet breaking out from a massive stellar envelope based on relativistic hydrodynamical simulations and post-process radiation transfer calculations in three dimensions. To investigate…
This paper focuses on the utility of various data transformation techniques, which might be under the principal component analysis (PCA) category, on exoplanet research. The first section introduces the methodological background of PCA and…
Principal Component Analysis (PCA) finds the best linear representation of data, and is an indispensable tool in many learning and inference tasks. Classically, principal components of a dataset are interpreted as the directions that…