Copula-based multivariate input modeling

Biller, Bahar; Corlu, Canan G.

doi:10.1016/j.sorms.2012.04.001

Cited by 27 publications

(8 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The first group random samples is generated by a classical inverse CDF transform method. The second group is also generated with a CDF inverse transform method but instead of generating random uncorrelated values from a uniform distribution between zero and one, they are generated with a random copula sampling method [17] to a chosen ''tau" degree of correlation. The correlated sets were generated for 10 different correlation coefficient degrees (s) ranging from almost 0 until 0.99.…”

Section: Reliability Method: Monte Carlo With Copula Random Samplingmentioning

confidence: 99%

“…Copula functions can be used for generating correlated values during a random sampling process. They allow to build joint distributions from two or more variables while maintaining the statistical properties of their marginal distributions [17]. All types of possible copulas (families) are derived from the Sklar theorem which states that every probability function can be written as a copula multivariate function of the uniformly transformed marginal values [10].…”

Section: Copula Correlation Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Soil stochastic parameter correlation impact in the piping erosion failure estimation of riverine flood defences

et al. 2016

View full text Add to dashboard Cite

Section: Reliability Method: Monte Carlo With Copula Random Samplingmentioning

confidence: 99%

Section: Copula Correlation Modelsmentioning

confidence: 99%

Soil stochastic parameter correlation impact in the piping erosion failure estimation of riverine flood defences

et al. 2016

View full text Add to dashboard Cite

“…e vines are commonly used to organize the decompositions [26]. e C-vine and D-vine [27] are two special cases of regular vines, as shown in Figure 2.…”

Section: Multivariate Probability Distribution Construction Bymentioning

confidence: 99%

Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations

Kong

Zhang

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

Stochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time or spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating nonlinear dependencies thus needs to be addressed based on the linear correlations. This paper first demonstrates the capture of nonlinear dependency by fitting various bivariate non-Gaussian copulas to limited data samples of structural material properties. The vine copula model is used to enable a flexible modeling of multiple nonlinear dependencies by mapping the linear correlations into the non-Gaussian copula parameters. A sequential search strategy is applied to achieve the estimate of numerous copula parameters, and a simplified algorithm is further designed for reliability involving stationary stochastic processes. The subset simulation is then adopted to efficiently generate random variables from the corresponding distribution for high reliability evaluation. Two examples including a frame structure with different stochastic material properties and a cantilever beam with spatially variable stochastic modulus are investigated to discuss the possible effects of nonlinear dependency on structural reliability. Since the dependency can be determined qualitatively from limited data, the proposed method provides a feasible way for reliability evaluation with prescriptions on correlated stochastic parameters.

show abstract

“…Various techniques have been applied to multivariate analysis in finance, relying on Independent Component Analysis to reduce dimensionality (Lu et al, 2009) and elliptical copula models to capture input dependencies (Biller and Corlu, 2012). These studies find incremental information gain in using multiple time series from the same domain.…”

Section: Prior Workmentioning

confidence: 99%

Extracting predictive information from heterogeneous data streams using Gaussian Processes

Ghoshal

Roberts

2016

View full text Add to dashboard Cite

Abstract. Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measure the performance gain, quantified in terms of Normalised Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson correlation, from fusing each of four separate data domains: time series technicals, sentiment analysis, options market data and broker recommendations. We show evidence that ARD kernels produce meaningful feature rankings that help retain salient inputs and reduce input dimensionality, providing a framework for sifting through financial complexity. We measure the performance gain from fusing each domain's heterogeneous data streams into a single probabilistic model. In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction.

show abstract

Copula-based multivariate input modeling

Cited by 27 publications

References 99 publications

Soil stochastic parameter correlation impact in the piping erosion failure estimation of riverine flood defences

Soil stochastic parameter correlation impact in the piping erosion failure estimation of riverine flood defences

Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations

Extracting predictive information from heterogeneous data streams using Gaussian Processes

Contact Info

Product

Resources

About