scholarly journals Empirical and non-parametric copula models with the cort R package

2020 ◽  
Vol 5 (56) ◽  
pp. 2653
Author(s):  
Oskar Laverny
Keyword(s):  
R Package ◽  
Copula Models ◽  
Parametric Copula ◽  
Non Parametric

scholarly journals Inference for copula modeling of discrete data: a cautionary tale and some facts

2017 ◽  
Vol 5 (1) ◽  
pp. 121-132 ◽  
Author(s):  
Olivier P. Faugeras
Keyword(s):  
Model Misspecification ◽  
Discrete Data ◽  
Cautionary Tale ◽  
Copula Models ◽  
Problematic Use ◽  
Copula Modeling ◽  
Copula Parameter ◽  
Parametric Copula

AbstractIn this note, we elucidate some of the mathematical, statistical and epistemological issues involved in using copulas to model discrete data. We contrast the possible use of (nonparametric) copula methods versus the problematic use of parametric copula models. For the latter, we stress, among other issues, the possibility of obtaining impossible models, arising from model misspecification or unidentifiability of the copula parameter.

scholarly journals npphen: an R-package for non-parametric reconstruction of vegetation phenology and anomaly detection using remote sensing

2018 ◽  
Author(s):  
Sergio A. Estay ◽  
Roberto O. Chávez
Keyword(s):  
Remote Sensing ◽  
Anomaly Detection ◽  
Land Surface ◽  
Relevant Information ◽  
R Package ◽  
Plant Ecology ◽  
Essential Biodiversity Variables ◽  
Cost Efficient ◽  
High Level ◽  
Non Parametric

AbstractFor ecologists, the challenge at using remote sensing tools is to convert spectral data into ecologically relevant information like abundance, productivity or traits distribution. Among these features, plant phenology is one of the most used variables in any study applying remote sensing to plant ecology and it has formally considered as one of the Essential Biodiversity Variables. Currently, satellite imagery make possible cost-efficient monitoring of land surface phenology (LSP), but methods applicable to different ecosystems are not available. Here, we introduce the ‘npphen’ R-package developed for remote sensing LSP reconstruction and anomaly detection using non-parametric techniques. The package implements basic and high-level functions for manipulating vector and raster data to obtain high resolution spatial and temporal LSP reconstructions. Advantages of ‘npphen’ are: its flexibility to describe any LSP pattern (suitable for any ecosystem), it handles time series or raster stacks with and without gaps, and it provides confidence interval for the expected LSP at yearly basis, useful to judge anomaly magnitudes. We present two study cases to show how ‘npphen’ can successfully reconstruct and map LSP and anomalies for contrasting ecosystems.

A Non-Parametric Method for Detecting Specificity Determining Sites in Protein Sequence Alignments

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Walter R Gilks ◽  
Chinying Wang
Keyword(s):  
Selective Pressure ◽  
Parametric Method ◽  
R Package ◽  
Sequence Alignments ◽  
Functional Specificity ◽  
Acid Properties ◽  
Rates Of Evolution ◽  
Amino Acid Properties ◽  
Principle Of Parsimony ◽  
Non Parametric

Specificity determining sites (SDSs) in alignments of protein sequences are sites at which subfamilies of the aligned sequences have been under differential selective pressure. Identifying SDSs is important because they are key in understanding the functional specificity of each subfamily. Differential selection at an SDS will result in differences between subfamilies in the distribution of amino-acids at that site. However, statistical analysis of such differences is complicated by phylogenetic relationships within each subfamily, which profoundly influence these differences. We develop a non-parametric approach to evaluating purely statistical SDS evidence in a sequence alignment, taking account of phylogeny through a novel tree-respecting randomisation based on the principle of parsimony. Our approach does not exploit bioinformatic measures based on amino-acid properties or rates of evolution, as do other methods. Our intention is thereby to supplement and strengthen other methods of SDS prediction, not to compete with them. Our methodology is implemented in the R package called SDSparsimony, freely downloadable from http://www.maths.leeds.ac.uk/%7Ewally.gilks/SDSparsimonyPackage/Welcome.html.

rNPBST: An R Package Covering Non-parametric and Bayesian Statistical Tests

2017 ◽  
pp. 281-292 ◽  
Author(s):  
Jacinto Carrasco ◽  
Salvador García ◽  
María del Mar Rueda ◽  
Francisco Herrera
Keyword(s):  
Statistical Tests ◽  
R Package ◽  
Non Parametric

scholarly journals Non-parametric Copula Estimation Under Bivariate Censoring

2015 ◽  
Vol 42 (4) ◽  
pp. 925-946 ◽  
Author(s):  
Svetlana Gribkova ◽  
Olivier Lopez
Keyword(s):  
Copula Estimation ◽  
Parametric Copula ◽  
Non Parametric
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