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Selection: with tag distance-correlation [13 articles] 

 

K-groups: a generalization of K-means clustering

  
(12 Nov 2017)

Abstract

We propose a new class of distribution-based clustering algorithms, called k-groups, based on energy distance between samples. The energy distance clustering criterion assigns observations to clusters according to a multi-sample energy statistic that measures the distance between distributions. The energy distance determines a consistent test for equality of distributions, and it is based on a population distance that characterizes equality of distributions. The k-groups procedure therefore generalizes the k-means method, which separates clusters that have different means. We propose two k-groups algorithms: k-groups by first variation; and k-groups by ...

 

Partial distance correlation with methods for dissimilarities

  
The Annals of Statistics, Vol. 42, No. 6. (December 2014), pp. 2382-2412, https://doi.org/10.1214/14-aos1255

Abstract

Distance covariance and distance correlation are scalar coefficients that characterize independence of random vectors in arbitrary dimension. Properties, extensions and applications of distance correlation have been discussed in the recent literature, but the problem of defining the partial distance correlation has remained an open question of considerable interest. The problem of partial distance correlation is more complex than partial correlation partly because the squared distance covariance is not an inner product in the usual linear space. For the definition of partial ...

 

Energy distance

  
Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 8, No. 1. (January 2016), pp. 27-38, https://doi.org/10.1002/wics.1375

Abstract

Energy distance is a metric that measures the distance between the distributions of random vectors. Energy distance is zero if and only if the distributions are identical, thus it characterizes equality of distributions and provides a theoretical foundation for statistical inference and analysis. Energy statistics are functions of distances between observations in metric spaces. As a statistic, energy distance can be applied to measure the difference between a sample and a hypothesized distribution or the difference between two or more samples ...

 

Fast computing for distance covariance

  
Technometrics (25 June 2015), pp. 0-0, https://doi.org/10.1080/00401706.2015.1054435

Abstract

Distance covariance and distance correlation have been widely adopted in measuring dependence of a pair of random variables or random vectors. If the computation of distance covariance and distance correlation is implemented directly accordingly to its definition then its computational complexity is O(n2) which is a disadvantage compared to other faster methods. In this paper we show that the computation of distance covariance and distance correlation of real valued random variables can be implemented by an O(n log n) algorithm and ...

 

Discussion of: Brownian distance covariance

  
The Annals of Applied Statistics, Vol. 3, No. 4. (5 December 2009), pp. 1295-1298, https://doi.org/10.1214/09-aoas312f

Abstract

Discussion on "Brownian distance covariance" by Gábor J. Székely and Maria L. Rizzo [<a href="/abs/1010.0297">arXiv:1010.0297</a>] ...

 

Measuring and testing dependence by correlation of distances

  
The Annals of Statistics, Vol. 35, No. 6. (28 December 2007), pp. 2769-2794, https://doi.org/10.1214/009053607000000505

Abstract

Distance correlation is a new measure of dependence between random vectors. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but unlike the classical definition of correlation, distance correlation is zero only if the random vectors are independent. The empirical distance dependence measures are based on certain Euclidean distances between sample elements rather than sample moments, yet have a compact representation analogous to the classical covariance and correlation. Asymptotic properties and applications in testing independence are discussed. Implementation of the test and Monte Carlo results are ...

 

Brownian distance covariance

  
The Annals of Applied Statistics, Vol. 3, No. 4. (6 Oct 2010), pp. 1236-1265, https://doi.org/10.1214/09-aoas312

Abstract

Distance correlation is a new class of multivariate dependence coefficients applicable to random vectors of arbitrary and not necessarily equal dimension. Distance covariance and distance correlation are analogous to product-moment covariance and correlation, but generalize and extend these classical bivariate measures of dependence. Distance correlation characterizes independence: it is zero if and only if the random vectors are independent. The notion of covariance with respect to a stochastic process is introduced, and it is shown that population distance covariance coincides with the covariance with respect to Brownian motion; thus, ...

 

Rejoinder: brownian distance covariance

  
The Annals of Applied Statistics, Vol. 3, No. 4. (5 Oct 2010), pp. 1303-1308, https://doi.org/10.1214/09-aoas312rej

Abstract

Rejoinder to "Brownian distance covariance" by Gábor J. Székely and Maria L. Rizzo [arXiv:1010.0297] ...

 

Supplementary materials for: a proposal for an integrated modelling framework to characterise habitat pattern

  
(2014)

Abstract

In Estreguil et al. (Environ Modell Softw 52, 176-191, 2014), an integrated modelling framework is proposed to characterise habitat pattern. The modelling approach is there exemplified by deriving a set of twelve indices aggregated into four categories: general landscape composition, habitat morphology, edge interface and connectivity. The easy and reproducible computability is ensured with the integrated use of publicly available software (GUIDOS free-download software, Conefor free software) and of newly programmed tools. A statistical analysis is then conducted using classical linear ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 10

  
(February 2014)
Keywords: database   dataset   dating   dddas   de-facto-standard   dead-wood   debris   debris-floods   debris-flows   deciduous   deciduous-forest   decision-making   decision-making-procedure   decision-support-system   decline   decline-effect   decline-symptomology   deep-reproducible-research   deep-uncertainty   definition   deforestation   degenerated-soil   deglaciation   degradation   degradation-velocity   dehesas   delonix-regia   democracy   dendrochronology   dendroctonus   dendroctonus-frontalis   dendroctonus-micans   dendroctonus-ponderosae   dendroctonus-pseudotsugae   dendroecology   dendrology   denmark   density-related-behaviour   deposition   derived-data   desalinisation   description   desertification   deserts   design-diversity   devil-in-details   diabetes   diabetes-mellitus   diagram-data   diameter-differentiation   dictionary   die-off   dieback   diesel   differentiation   digital-preservation   digital-society   dimensional-analysis   dimensionality-reduction   dimensionless   dioryctria-splendidella   diospyros-kaki   diospyros-spp   diospyros-virginiana   diplodia-pinea   diprion-pini   dipteryx-panamensis   direct-reciprocity   disaster-recovery   disaster-response   disasters   discharge   disciplinary-barrier   disconcerting-learning   discount-rate   disease   diseases   disjunction   dispersal   dispersal-limitation   dispersal-models   dissent   distance-analysis   distance-correlation   distilled-gin   distribution   distribution-limit   disturbance-ecology   disturbance-interactions   disturbances   diversity   django   dna   dna-fingerprinting   dobrogea   dodonaea-viscosa   dormancy   dormouse   inrmm-list-of-tags  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

 

A proposal for an integrated modelling framework to characterise habitat pattern

  
Environmental Modelling & Software, Vol. 52 (February 2014), pp. 176-191, https://doi.org/10.1016/j.envsoft.2013.10.011

Abstract

[Highlights] [::] Habitat pattern characterisation as methodological guidance for fragmentation assessments (applied in Europe). [::] Reproducible integration of three landscape models with GIS and semantic array programming. [::] Four families indices: landscape composition, edge interface, habitat morphology and connectivity. [::] New indices: edge interface context of morphological shapes; Power Weighted Probability of Dispersal family for connectivity. [::] Nonlinear statistical correlation analysis based on Brownian Distance Correlation. [Abstract] Harmonized information on habitat pattern, fragmentation and connectivity is one among the reporting needs of the biodiversity policy agenda. This paper ...

 

Detecting general multi-dimensional nonlinear correlations: the module "dist_corr" of the Mastrave modelling library

  
In Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modelling (2012), https://doi.org/10.6084/m9.figshare.92645

Abstract

Linear correlation analysis of complex nonlinear physical or computationally derived quantities - despite straightforward to be implemented with the help of basic numerical tools - may be far sub-optimal in assessing the actual strength of existing relationships between quantities. Moreover, in many applications not only the correlation between pairs of quantities is of interest, but also the more general correlation between a certain group of quantities and another one. Multi-dimensional nonlinear correlation analysis may offer elegant and concise ways of exploring ...

 

Finding correlations in big data

  
Nature Biotechnology, Vol. 30, No. 4. (10 April 2012), pp. 334-335, https://doi.org/10.1038/nbt.2182

Abstract

In today's era of large data sets, statistical methods that facilitate exploratory analyses to detect patterns and generate hypotheses are critical to progress in biology. Last year, David Reshef and colleagues published a new approach to such analysis, called maximal information criteria or MIC (Science 334, 1518–1524, 2011). Nature Biotechnology solicited comments from several practitioners versed in data-intensive biological research. Their responses not only highlight the appeal of methods like MIC for biological research, but also raise some important reservations as ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/distance-correlation

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.