From MFKP_wiki

Jump to: navigation, search

Selection: with tag nonlinear-correlation [21 articles] 


Kernel-based measures of association

Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 10, No. 2. (March 2018), e1422,


Measures of association have been widely used for describing statistical relationships between two sets of variables. Traditionally, such association measures focus on specialized settings. Based on an in‐depth summary of existing common measures, we present a general framework for association measures that unifies existing methods and novel extensions based on kernels, including practical solutions to computational challenges. Specifically, we introduce association screening and variable selection via maximizing kernel‐based association measures. We also develop a backward dropping procedure for feature selection when ...


A study of the relation of meteorological variables to monthly provincial area burned by wildfire in Canada (1953-80)

Journal of Applied Meteorology and Climatology, Vol. 27, No. 4. (1 April 1988), pp. 441-452,<0441:asotro>;2


The relation between meteorological variables and the monthly area burned by wildfire from May to August 1953–80 in nine Canadian “provinces” was investigated. A purely statistical approach to estimating the monthly provincial area burned, using meteorological variables as predictors, succeeded in explaining 30% of the variance west of Lake Nipigon and about 11% east of Lake Nipigon. [\n] Long sequences of days with less than 1.5 mm of rain or days with relative humidities less than 60% proved to have the highest ...


Partial distance correlation with methods for dissimilarities

The Annals of Statistics, Vol. 42, No. 6. (December 2014), pp. 2382-2412,


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,


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,


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 ...


(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 26

(February 2014)
Keywords: inrmm-list-of-tags   nectaroscordum-siculum   nectria-coccinea   negative-emission   negative-emissions   negative-learning   negative-studies   neglecting-non-monetary-criteria   negotiation   neighbourhood-analysis   nematus-melanaspis   nematus-oligospilus   nemoral-climate   neocallitropsis-pancheri   neodiprion-sertifer   neofusicoccum-parvum   neogene   neonicotinoid   nepal   nephelium-lappaceum   nerium-oleander   nested-loops-and-conditional-structures   netherlands   network-representation-capability   networks   neural-networks   neuro-dynamic-programming   neuroterus-spp   new-forested-areas   new-species   new-zealand   niche-limits   niche-model   niche-modelling   niche-sourcing   nickel   nigeria   nitrogen   nitrogen-deposition   nitrogen-fixation   nitrogen-leaching   nitrogen-partitioning   no-analog-pattern   no-analogue   no-free-lunch-theorem   nolina-recurvata   non-array-oriented   non-equilibrium   non-linearity   non-semantic-software-errors   non-stationarity   non-wood-products   nonadditive-measures   nonideal-neurons   nonlinear-correlation   nonlinear-response-to-bioclimatic-predictors   nonmarket-impacts   nonsteady-flame-convection   north-africa   north-america   northern-europe   northern-hemisphere   norway   not-automatic-workflow   notation   notation-as-a-tool-of-thought   nothofagus-cunninghamii   nothofagus-glauca   nothofagus-nervosa   nothofagus-procera   nothofagus-pumilio   nothofagus-spp   notholithocarpus-densiflorus   nothotsuga-spp   nreap-2020   nuclear-disasters   numerical-analysis   numpy   nurse-species   nut-producing-plants   nutrient-gradient   nutrient-recommendations   nutrient-rich-soil   nutrients   nutritional-composition   nyssa-spp   nyssa-sylvatica   oak-decline   oak-hornbeam-forest   oak-shake   object-classification   object-detection   object-oriented-programming   occam-razor   occupancy-vs-detection   ocean-acidification   ocean-circulation   oceans   ochroma-pyramidale   oenothera-spp  


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 ( ). ...


Discussion of: Brownian distance covariance

The Annals of Applied Statistics, Vol. 3, No. 4. (5 December 2009), pp. 1295-1298,


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


Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Neural Computation In Neural Computation, Vol. 10, No. 5. (1 July 1998), pp. 1299-1319,


A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map - for instance, the space of all possible five-pixel products in 16 x 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition. A new method for performing a nonlinear form ...


Nonlinear principal component analysis: neural network models and applications



Nonlinear principal component analysis (NLPCA) as a nonlinear generalisation of standard principal component analysis (PCA) means to generalise the principal components from straight lines to curves. This chapter aims to provide an extensive description of the autoassociative neural network approach for NLPCA. Several network architectures will be discussed including the hierarchical, the circular, and the inverse model with special emphasis to missing data. Results are shown from applications in the field of molecular biology. This includes metabolite data analysis of a ...


Nonlinear PCA: a new hierarchical approach

In Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN) (2002), pp. 439-444

Measuring and testing dependence by correlation of distances

The Annals of Statistics, Vol. 35, No. 6. (28 December 2007), pp. 2769-2794,


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,


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, ...


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



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 ...


A proposal for an integrated modelling framework to characterise habitat pattern

Environmental Modelling & Software, Vol. 52 (February 2014), pp. 176-191,


[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 ...


Multifractal Detrended Cross-Correlation Analysis of sunspot numbers and river flow fluctuations

Physica A: Statistical Mechanics and its Applications, Vol. 389, No. 21. (22 November 2010), pp. 4942-4957,


We use the Detrended Cross-Correlation Analysis (DCCA) to investigate the influence of sun activity represented by sunspot numbers on one of the climate indicators, specifically rivers, represented by river flow fluctuation for Daugava, Holston, Nolichucky and French Broad rivers. The multifractal Detrended Cross-Correlation Analysis (MF-DXA) shows that there exist some crossovers in the cross-correlation fluctuation function versus time scale of the river flow and sunspot series. One of these crossovers corresponds to the well-known cycle of solar activity demonstrating a universal ...


Impact of variations in solar activity on hydrological decadal patterns in northern Italy

Journal of Geophysical Research, Vol. 113, No. D12. (19 June 2008),


Using spectral and statistical analyses of discharges and basin average precipitation rates acquired over the Po River since the early 1800s, we investigate the impact of variations in solar activity on hydrological decadal patterns over northern Italy. Wet and dry periods appear to alternate in accordance with polarized sunspot cycles. Intriguingly, a solar signature on Po River discharges is detected to be highly significant since the late 1800s, before the onset of sunspots hyperactivity established by the middle 1900s. In particular, ...


Do we need land-cover data to model species distributions in Europe?

Journal of Biogeography, Vol. 31, No. 3. (1 March 2004), pp. 353-361,


Aim  To assess the influence of land cover and climate on species distributions across Europe. To quantify the importance of land cover to describe and predict species distributions after using climate as the main driver. Location  The study area is Europe. Methods  (1) A multivariate analysis was applied to describe land-cover distribution across Europe and assess if the land cover is determined by climate at large spatial scales. (2) To evaluate the importance of land cover to predict species distributions, we ...


Multi-disciplinary forest fire danger assessment in Europe: the potential to integrate long-term drought information

International Journal of Spatial Data Infrastructures Research, Vol. 7 (2012), pp. 300-322,


A key motivation for multi-disciplinary collaborations is the inclusion of data and knowledge from contributing disciplines for the further development of existing models. The objective of this research is to evaluate the potential of using drought information from the European Drought Observatory (EDO) to complement the forest fire danger assessment of the European Forest Fire Information System (EFFIS). Drought conditions are provided through the Standardized Precipitation Index (SPI), which is a spatially invariant and probabilistic year-round index based on precipitation alone. ...


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),


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 ...


Localizing general models based on local indices of spatial association

European Journal of Forest Research, Vol. 126, No. 2. (1 April 2007), pp. 279-289,


A general regression model for large areas may have poor statistical properties for smaller sub-regions. In this study, we test the local indicators of spatial association (LISA) in the selection of localization areas of a general regression model. We present four different LISAs: Moran’s I i , Geary’s c i , G i , and G i *. These indices show if there is a cluster of similar values in the data (Moran’s I i and Geary’s c i ) or ...


Finding correlations in big data

Nature Biotechnology, Vol. 30, No. 4. (10 April 2012), pp. 334-335,


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.

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.