scholarly journals Multivariate Outlier Detection in Precipitation Series by Using Two-Dimensional Correlation

2021 ◽  
Author(s):  
Mutlu Yaşar ◽  
Fatih Dikbaş
Keyword(s):  
Correlation Method ◽  
Descriptive Statistics ◽  
Data Sets ◽  
Two Dimensional ◽  
Outlier Analysis ◽  
Multivariate Outlier Detection ◽  
Multivariate Outliers ◽  
Modelling Studies ◽  
Supplementary Material

Abstract The accuracy of descriptive statistics might be influenced by the existence of outliers in data sets. An observation which might not be considered as an outlier in the univariate case might be a multivariate outlier. Therefore, determination of outliers might make multivariate analysis more robust by providing an opportunity for making required corrections before modelling studies. This paper presents the implementation of the two-dimensional correlation method in the determination of multivariate outliers among the observations of six precipitation stations in Turkey. The two-dimensional correlation method considers the averages of the parts of the whole series instead of the average of the whole series and enables determination of the location of the outlier in the compared series. The obtained results point out that an outlier analysis for hydrologic variables should consider the two-directional behavior and the presented two-dimensional correlation method proves to be a strong alternative to be used in outlier and irregularity detection studies even with a limited number of available data. The 2DCorr software used in the study is freely provided as a supplementary material.

scholarly journals Multivariate Outlier Detection by Using Two-Dimensional Correlation

2021 ◽  
Author(s):  
Fatih Dikbas
Keyword(s):  
Correlation Method ◽  
Descriptive Statistics ◽  
Data Sets ◽  
Two Dimensional ◽  
Outlier Analysis ◽  
Multivariate Outlier Detection ◽  
Multivariate Outliers ◽  
Modelling Studies ◽  
Supplementary Material

Abstract The accuracy of descriptive statistics might be influenced by the existence of outliers in data sets. An observation which might not be considered as an outlier in the univariate case might be a multivariate outlier. Therefore, determination of outliers might make multivariate analysis more robust by providing an opportunity for making required corrections before modelling studies. This paper presents the implementation of the two-dimensional correlation method in the determination of multivariate outliers among the observations of six precipitation stations in Turkey. The two-dimensional correlation method considers the averages of the parts of the whole series instead of the average of the whole series and enables determination of the location of the outlier in the compared series. The obtained results point out that an outlier analysis for hydrologic variables should consider the two-directional behavior and the presented two-dimensional correlation method proves to be a strong alternative to be used in outlier and irregularity detection studies even with a limited number of available data. The 2DCorr software used in the study is freely provided as a supplementary material.

Generalized Two-Dimensional Correlation Method Applicable to Infrared, Raman, and other Types of Spectroscopy

1993 ◽  
Vol 47 (9) ◽  
pp. 1329-1336 ◽  
Author(s):  
I. Noda
Keyword(s):  
Correlation Method ◽  
External Perturbation ◽  
Data Sets ◽  
Two Dimensional ◽  
Electromagnetic Probe ◽  
Wide Range ◽  
Potential Applications ◽  
Spectral Variable ◽  
Spectroscopic Tool ◽  
Ir And Raman

A two-dimensional (2D) correlation method generally applicable to various types of spectroscopy, including IR and Raman spectroscopy, is introduced. In the proposed 2D correlation scheme, an external perturbation is applied to a system while being monitored by an electromagnetic probe. With the application of a correlation analysis to spectral intensity fluctuations induced by the perturbation, new types of spectra defined by two independent spectral variable axes are obtained. Such two-dimensional correlation spectra emphasize spectral features not readily observable in conventional one-dimensional spectra. While a similar 2D correlation formalism has already been developed in the past for analysis of simple sinusoidally varying IR signals, the newly proposed formalism is designed to handle signals fluctuating as an arbitrary function of time, or any other physical variable. This development makes the 2D correlation approach a universal spectroscopic tool, generally applicable to a very wide range of applications. The basic property of 2D correlation spectra obtained by the new method is described first, and several spectral data sets are analyzed by the proposed scheme to demonstrate the utility of generalized 2D correlation spectra. Potential applications of this 2D correlation approach are then explored.

Use of Image-Processing Tools for Texture Analysis of High-Energy X-ray Synchrotron Data

1998 ◽  
Vol 31 (5) ◽  
pp. 647-653 ◽  
Author(s):  
R. Fisker ◽  
H. F. Poulsen ◽  
J. Schou ◽  
J. M. Carstensen ◽  
S. Garbe
Keyword(s):  
Image Processing ◽  
Nonlinear Least Squares ◽  
High Energy ◽  
Three Dimensions ◽  
Polycrystalline Materials ◽  
Data Sets ◽  
Two Dimensional ◽  
X Ray Diffraction ◽  
X Ray

The introduction of synchrotron beamlines for high-energy X-ray diffraction raises new possibilities for texture determination of polycrystalline materials. The local texture can be mapped out in three dimensions and texture developments can be studiedin situin complicated environments. However, it is found that a full alignment of the two-dimensional detector used in many cases is impractical and that data-sets are often partially subject to geometric restrictions. Estimating the parameters of the traces of the Debye–Scherrer cones on the detector therefore becomes a concern. Moreover, the background may vary substantially on a local scale as a result of inhomogeneities in the sample environmentetc. A set of image-processing tools has been employed to overcome these complications. An automatic procedure for estimating the parameters of the traces (taken as ellipses) is described, based on a combination of a circular Hough transform and nonlinear least-squares fitting. Using the estimated ellipses the background is subtracted and the intensity along the Debye–Scherrer cones is integrated by a combined fit of the local diffraction pattern. The corresponding algorithms are presented together with the necessary coordinate transform for pole-figure determination. The image-processing tools may be useful for the analysis of noisy or partial powder diffraction data-sets in general, provided flat two-dimensional detectors are used.

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

scholarly journals Constraints on neutrino non-standard interactions from LHC data with large missing transverse momentum

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
DianYu Liu ◽  
ChuanLe Sun ◽  
Jun Gao
Keyword(s):  
Transverse Momentum ◽  
Neutral Current ◽  
Data Sets ◽  
Leading Order ◽  
Missing Transverse Momentum ◽  
Upper Limit ◽  
Theoretical Predictions ◽  
Parton Showering ◽  
Single Jet

Abstract The possible non-standard interactions (NSIs) of neutrinos with matter plays important role in the global determination of neutrino properties. In our study we select various data sets from LHC measurements at 13 TeV with integrated luminosities of 35 ∼ 139 fb−1, including production of a single jet, photon, W/Z boson, or charged lepton accompanied with large missing transverse momentum. We derive constraints on neutral-current NSIs with quarks imposed by different data sets in a framework of either effective operators or simplified Z′ models. We use theoretical predictions of productions induced by NSIs at next-to-leading order in QCD matched with parton showering which stabilize the theory predictions and result in more robust constraints. In a simplified Z′ model we obtain a 95% CLs upper limit on the conventional NSI strength ϵ of 0.042 and 0.0028 for a Z′ mass of 0.2 and 2 TeV respectively. We also discuss possible improvements from future runs of LHC with higher luminosities.

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