Robust detection of discordant sites in regional frequency analysis

Neykov, N. M.; Neytchev, P. N.; Gelder, Pieter van; Todorov, Valentin

doi:10.1029/2006wr005322

Cited by 23 publications

(25 citation statements)

References 9 publications

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“…In this study, we first normalized annual maximum precipitation at each station by station mean annual maximum precipitation, and pooled the normalized data by climate regions for two time periods : 1951-1980 and 1981-2013. We used the robust discordance test designed by Neykov et al (2007) to identify stations whose data distributions are significantly different from the rest in the region. Using this test, we eliminated about 4 % of the 3967 stations.…”

Section: Regional Frequency Analysismentioning

confidence: 99%

Changing characteristics of precipitation for the contiguous United States

2015

Climatic Change

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Using the US collection from the Global Historical Climatology Network Daily (GHCN-D) precipitation data for the contiguous United States (CONUS), this study examines the changing characteristics of precipitation during 1951-2013. In addition to mean precipitation, all precipitation events are divided into three categories: light, moderate, and heavy based on percentile thresholds. The historical trends are established for precipitation total, frequency and intensity, as well as for total and frequency of different intensity categories. Results show that from 1951 to 2013, mean precipitation increased at 1.66 % per decade, a higher rate than previous estimates. About one third of the increase is attributed to frequency change, whereas the other two thirds are attributed to an intensity increase. There was a slight decrease in light precipitation, a small increase in moderate precipitation, and much higher increase for heavy precipitation. Spatially, eastern and northern parts of the CONUS experienced higher rates of increase, whereas western regions experienced less increase. A statistically significant positive correlation exists between mean precipitation and precipitation change, suggesting the wet regions experienced more precipitation increase than dry regions. Seasonally, precipitation increased most for the fall, less in other seasons. Particularly, there were significant decreasing trends in summer precipitation for many parts of western and central CONUS. Regional frequency analysis is used to examine the change in extreme precipitation events with return intervals longer than a year. Results show that extreme precipitation events increased for most of the CONUS with the exception of the west region. These changes were a result of both a shift in the mean state and the shape of the precipitation data distribution.Climatic Change

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Section: Regional Frequency Analysismentioning

confidence: 99%

Changing characteristics of precipitation for the contiguous United States

2015

Climatic Change

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“…Applications of the MCD are numerous. We mention recent applications in finance and econometrics,63, 64 medicine,65 quality control,66 geophysics,67 image analysis68, 69 and chemistry,70 but this list is far from complete.…”

Section: Applicationsmentioning

confidence: 99%

Minimum covariance determinant

Hubert

Debruyne

2009

WIREs Computational Stats

212

142

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The minimum covariance determinant (MCD) estimator is a highly robust estimator of multivariate location and scatter. It can be computed efficiently with the FAST‐MCD algorithm of Rousseeuw and Van Driessen. Since estimating the covariance matrix is the cornerstone of many multivariate statistical methods, the MCD has also been used to develop robust and computationally efficient multivariate techniques. In this paper, we review the MCD estimator, along with its main properties such as affine equivariance, breakdown value, and influence function. We discuss its computation, and list applications and extensions of the MCD in theoretical and applied multivariate statistics. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods

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“…There are many applications of the MCD, for instance, in finance and econometrics (Gambacciani & Paolella, ; Welsh & Zhou, ; Zaman, Rousseeuw, & Orhan, ), medicine (Prastawa, Bullitt, Ho, & Gerig, ), quality control (Jensen, Birch, & Woodal, ), geophysics (Neykov, Neytchev, Van Gelder, & Todorov, ), geochemistry (Filzmoser, Garrett, & Reimann, ), image analysis (Lu, Wang, Kong, Zhang, & Zhang, ; Vogler, Goldenstein, Stolfi, Pavlovic, & Metaxas, ) and chemistry (van Helvoort, Filzmoser, & van Gaans, ), but this list is far from complete.…”

Section: Applicationsmentioning

confidence: 99%

Minimum covariance determinant and extensions

Hubert

Debruyne

Rousseeuw

2017

WIREs Computational Stats

160

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The minimum covariance determinant (MCD) method is a highly robust estimator of multivariate location and scatter, for which a fast algorithm is available. Since estimating the covariance matrix is the cornerstone of many multivariate statistical methods, the MCD is an important building block when developing robust multivariate techniques. It also serves as a convenient and efficient tool for outlier detection. The MCD estimator is reviewed, along with its main properties such as affine equivariance, breakdown value, and influence function. We discuss its computation, and list applications and extensions of the MCD in applied and methodological multivariate statistics. Two recent extensions of the MCD are described. The first one is a fast deterministic algorithm which inherits the robustness of the MCD while being almost affine equivariant. The second is tailored to high-dimensional data, possibly with more dimensions than cases, and incorporates regularization to prevent singular matrices.

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Robust detection of discordant sites in regional frequency analysis

Cited by 23 publications

References 9 publications

Changing characteristics of precipitation for the contiguous United States

Changing characteristics of precipitation for the contiguous United States

Minimum covariance determinant

Minimum covariance determinant and extensions

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