Emulation of Poincaré return maps with Gaussian Kriging models

In this paper we investigate the use of Gaussian emulators to give an accurate and computationally fast method to approximate return maps, a tool used to study the dynamics of differential equations. One advantage of emulators over other approximation techniques is that they encode deterministic data exactly, so where values of the return map are known these are also outputs of the emulator output, another is that emulators allow us to simultaneously emulate a parameterized family of ODEs giving a tool to assess the behavior of perturbed systems. The methods introduced here are illustrated using two well-known dynamical systems: The Rossler equations, and the Billiard system. We show that the method can be used to look at return maps and discuss the further implications for full computation of differential equation outputs

Estimation of Stress-Strength model in the Generalized Linear Failure Rate Distribution

In this paper, we study the estimation of $R=P [Y < X ]$, also so-called the stress-strength model, when both $X$ and $Y$ are two independent random variables with the generalized linear failure rate distributions, under different assumptions about their parameters. We address the maximum likelihood estimator (MLE) of $R$ and the associated asymptotic confidence interval. In addition, we compute the MLE and the corresponding Bootstrap confidence interval when the sample sizes are small. The Bayes estimates of $R$ and the associated credible intervals are also investigated. An extensive computer simulation is implemented to compare the performances of the proposed estimators. Eventually, we briefly study the estimation of this model when the data obtained from both distributions are progressively type-II censored. We present the MLE and the corresponding confidence interval under three different progressive censoring schemes. We also analysis a set of real data for illustrative purpose.Comment: 31 pages, 2 figures, preprin

Approximating multivariate distributions with vines

In a series of papers, Bedford and Cooke used vine (or pair-copulae) as a graphical tool for representing complex high dimensional distributions in terms of bivariate and conditional bivariate distributions or copulae. In this paper, we show that how vines can be used to approximate any given multivariate distribution to any required degree of approximation. This paper is more about the approximation rather than optimal estimation methods. To maintain uniform approximation in the class of copulae used to build the corresponding vine we use minimum information approaches. We generalised the results found by Bedford and Cooke that if a minimal information copula satis¯es each of the (local) constraints (on moments, rank correlation, etc.), then the resulting joint distribution will be also minimally informative given those constraints, to all regular vines. We then apply our results to modelling a dataset of Norwegian financial data that was previously analysed in Aas et al. (2009)

Symmetry properties of subdivision graphs

The subdivision graph $S(\Sigma)$ of a graph $\Sigma$ is obtained from $\Sigma$ by `adding a vertex' in the middle of every edge of \Si. Various symmetry properties of $\S(\Sigma)$ are studied. We prove that, for a connected graph $\Sigma$, $S(\Sigma)$ is locally $s$-arc transitive if and only if $\Sigma$ is $\lceil\frac{s+1}{2}\rceil$-arc transitive. The diameter of $S(\Sigma)$ is $2d+\delta$, where $\Sigma$ has diameter $d$ and $0\leqslant \delta\leqslant 2$, and local $s$-distance transitivity of $\S(\Sigma)$ is defined for $1\leqslant s\leqslant 2d+\delta$. In the general case where $s\leqslant 2d-1$ we prove that $S(\Sigma)$ is locally $s$-distance transitive if and only if $\Sigma$ is $\lceil\frac{s+1}{2}\rceil$-arc transitive. For the remaining values of $s$, namely $2d\leqslant s\leqslant 2d+\delta$, we classify the graphs $\Sigma$ for which $S(\Sigma)$ is locally $s$-distance transitive in the cases, $s\leqslant 5$ and $s\geqslant 15+\delta$. The cases $\max\{2d, 6\}\leqslant s\leqslant \min\{2d+\delta, 14+\delta\}$ remain open