Vine Copula Based Modeling

Czado, Claudia; Nagler, Thomas

doi:10.1146/annurev-statistics-040220-101153

Cited by 73 publications

(24 citation statements)

References 103 publications

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“…( 22 ). It describes the relationship between two RVs when one goes to extreme values and what the behavior of the other one is (Czado and Nagler 2022 ). We can conclude from this Fig.…”

Section: Resultsmentioning

confidence: 99%

A spatial copula interpolation in a random field with application in air pollution data

Thakur

Das

Shubhashree

2022

Model. Earth Syst. Environ.

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Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Copulas can capture different types of joint tail characteristics beyond the Gaussian paradigm. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging. Especially when interpolating along a spatial surface, the boundary points also demand focus in forming a neighborhood. As a result, importing the concept of hierarchical clustering on the spatial random field is necessary for developing the copula model with the interface of the Expectation-Maximization algorithm and concurrently utilizing the idea of the Bayesian framework. This article introduces a spatial cluster-based C-vine copula and a modified Gaussian distance kernel to derive a novel spatial probability distribution. To make spatial copula interpolation compatible and efficient, we estimate the parameter by employing different techniques. We apply the proposed spatial interpolation approach to the air pollution of Delhi as a crucial circumstantial study to demonstrate this newly developed novel spatial estimation technique.

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“…( 22 ). It describes the relationship between two RVs when one goes to extreme values and what the behavior of the other one is (Czado and Nagler 2022 ). We can conclude from this Fig.…”

Section: Resultsmentioning

confidence: 99%

A spatial copula interpolation in a random field with application in air pollution data

Thakur

Das

Shubhashree

2022

Model. Earth Syst. Environ.

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“…In this context, it could also be useful to use other techniques to fit the marginal distributions, such as the techniques based on Spline Hermite quasi-interpolation presented in [64]. Moreover, we plan to extend the method by considering a different strategy to reduce the dimensionality of SITS and extend the study by using other families of copula as Vine copulas [44,65], which have proven to work very well in the presence of large datasets. Finally, the use of a multi-source scenario in which optical (Sentinel-2) SITS can be combined with Synthetic Aperture Radar (Sentinel-1) SITS data may represent a future development.…”

Section: Discussionmentioning

confidence: 99%

Sentinel-2 Satellite Image Time-Series Land Cover Classification with Bernstein Copula Approach

2022

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A variety of remote sensing applications call for automatic optical classification of satellite images. Recently, satellite missions, such as Sentinel-2, allow us to capture images in real-time of the Earth’s scenario. The classification of this large amount of data requires increasingly precise and fast methods, which must take into account not only the spectral features dependence of each individual image but also that of the temporal ones. Copulas are an excellent statistical tool, able to model joint distributions between even random variables. In this paper, we propose a new approach for Satellite Image Time-Series (SITS) land cover classification, which combines the matrix factorization to reduce the dimensionality of the data and the use of copulas distribution to model the dependencies. We will show how the use of particular copulas can improve the accuracy of classification compared to the latest methodologies used for the classification task, such as those using Neural Networks. Experiments were conducted at a study site located on Reunion Island, using Sentinel-2 SITS data. Results are compared to those achieved by several approaches commonly used to address SITS-based land cover mapping and show that the use of copulas, in combination with the matrix factorization, achieved the highest classification yield compared to competing approaches.

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“…This is called proximity condition. For a more comprehensive introduction to vines, see Czado (2019) and a recent review Czado and Nagler (2021).…”

Section: R E F E R E N C E Smentioning

confidence: 99%

Two‐part D‐vine copula models for longitudinal insurance claim data

Yang

Czado

2022

Scandinavian J Statistics

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In short-term nonlife (e.g., car and homeowner) insurance, policies are renewed yearly. Insurance companies typically keep track of each policyholder's claims per year, resulting in longitudinal data. Efficient modeling of time dependence in longitudinal claim data will improve the prediction of future claims needed for routine actuarial practice, such as ratemaking. Insurance claim data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. This two-part data structure leads to difficulties in applying established models for longitudinal data. In this paper, we propose a two-part D-vine copula model to study longitudinal mixed claim data. We build two stationary D-vine copulas. One is used to model the time dependence in binary outcomes resulting from whether or not a claim has occurred. The other studies the dependence in the claim size given occurrence. Under the proposed model, the prediction of the probability of making claims and the quantiles of severity given occurrence is straightforward. We use our approach to investigate a dataset from the Local Government Property Insurance Fund in the state of Wisconsin.

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Vine Copula Based Modeling

Cited by 73 publications

References 103 publications

A spatial copula interpolation in a random field with application in air pollution data

A spatial copula interpolation in a random field with application in air pollution data

Sentinel-2 Satellite Image Time-Series Land Cover Classification with Bernstein Copula Approach

Two‐part D‐vine copula models for longitudinal insurance claim data

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