• Corpus ID: 3846629

Modeling and generating multi-variate-attribute random vectors using a new simulation method combined with NORTA algorithm

@inproceedings{Niavarani2013ModelingAG,
  title={Modeling and generating multi-variate-attribute random vectors using a new simulation method combined with NORTA algorithm},
  author={Niavarani and Ajr Smith},
  year={2013}
}
The NORmal-To-Anything (NORTA) algorithm requires a correlation matrix of multivariate normal variables to convert a multivariate normal vector to any other distribution. This paper presents a new simulation method that works in combination with the NORTA algorithm yet avoids having to solve some complicated equations which need to be solved to achieve this matrix. The performance of the proposed method is investigated in three examples and the results indicate that the proposed method works… 

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References

SHOWING 1-10 OF 29 REFERENCES

Generating correlation matrices for normal random vectors in NORTA algorithm using artificial neural networks

TLDR
The use of artificial neural networks, called Perceptron, is suggested to solve the so-called correlation-matching problem and the applicability of the proposed methodology is described and the results obtained from the proposed method to the ones from solving the equations numerically are compared.

Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations

TLDR
Empirical comparisons show that the control-variate variance-reduction technique improves the algorithm's convergence speed as well as its robustness.

An Approximate Method for Sampling Correlated Random Variables From Partially-Specified Distributions

TLDR
This paper presents an algorithm for generating correlated vectors of random numbers using a combination of Cholesky decomposition and Gauss-Newton iteration for cost analysis.

Generating random deviates from multivariate Pearson distributions

Chessboard Distributions and Random Vectors with Specified Marginals and Covariance Matrix

TLDR
The approach is used to rigorously establish that there are sets of marginals with feasible covariance matrix that the NORTA method cannot match, and how to modify the initialization phase of NORTA so that it will exactly match the marginals, and approximately match the desired covariance Matrix.

Multivariate input modeling with johnson distributions

TLDR
A new method is introduced that matches the first four marginal moments and the correlation structure of a given set of sample data, allowing computationally efficient parameter estimation and random-vector generation in multivariate simulation input modeling.

A Simple Scheme for Generating Multivariate Gamma Distributions with Non-Negative Covariance Matrix

To generate the gamma distributed random vector ? (of dimension K) the scheme η = ξ(1) + Tξ(2) is considered where ξ(1) (of dimension K) and ξ(2) (of dimension N) consist of independently gamma

Non-Uniform Random Variate Generation

TLDR
This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.