2,102 research outputs found
A generalization of Marshall-Olkin bivariate Pareto model and its applications in shock and competing risk models
Statistical inference for extremes has been a subject of intensive research during the last years. In this paper, we generalize the Marshall-Olkin bivariate Pareto distribution. In this case, a new bivariate distribution is introduced by compounding the Pareto Type and geometric distributions. This new bivariate distribution has natural interpretations and can be applied in fatal shock models or in competing risks models. We call the new proposed model Marshall-Olkin bivariate Pareto-geometric (MOBPG) distribution, and then investigate various properties of the new distribution. This model has five unknown parameters and the maximum likelihood estimators cannot be afforded in explicit structure. We suggest to use the EM algorithm to calculate the maximum likelihood estimators of the unknown parameters, and this structure is quite flexible. Also, Monte Carlo simulations are performed to investigate the effectiveness of the proposed algorithm. Finally, we analyze a real data set to investigate our purposes
Bayesian threshold selection for extremal models using measures of surprise
Statistical extreme value theory is concerned with the use of asymptotically
motivated models to describe the extreme values of a process. A number of
commonly used models are valid for observed data that exceed some high
threshold. However, in practice a suitable threshold is unknown and must be
determined for each analysis. While there are many threshold selection methods
for univariate extremes, there are relatively few that can be applied in the
multivariate setting. In addition, there are only a few Bayesian-based methods,
which are naturally attractive in the modelling of extremes due to data
scarcity. The use of Bayesian measures of surprise to determine suitable
thresholds for extreme value models is proposed. Such measures quantify the
level of support for the proposed extremal model and threshold, without the
need to specify any model alternatives. This approach is easily implemented for
both univariate and multivariate extremes.Comment: To appear in Computational Statistics and Data Analysi
Nonparametric estimation of extremal dependence
There is an increasing interest to understand the dependence structure of a
random vector not only in the center of its distribution but also in the tails.
Extreme-value theory tackles the problem of modelling the joint tail of a
multivariate distribution by modelling the marginal distributions and the
dependence structure separately. For estimating dependence at high levels, the
stable tail dependence function and the spectral measure are particularly
convenient. These objects also lie at the basis of nonparametric techniques for
modelling the dependence among extremes in the max-domain of attraction
setting. In case of asymptotic independence, this setting is inadequate, and
more refined tail dependence coefficients exist, serving, among others, to
discriminate between asymptotic dependence and independence. Throughout, the
methods are illustrated on financial data.Comment: 22 pages, 9 figure
A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes
In modeling spatial extremes, the dependence structure is classically
inferred by assuming that block maxima derive from max-stable processes.
Weather stations provide daily records rather than just block maxima. The point
process approach for univariate extreme value analysis, which uses more
historical data and is preferred by some practitioners, does not adapt easily
to the spatial setting. We propose a two-step approach with a composite
likelihood that utilizes site-wise daily records in addition to block maxima.
The procedure separates the estimation of marginal parameters and dependence
parameters into two steps. The first step estimates the marginal parameters
with an independence likelihood from the point process approach using daily
records. Given the marginal parameter estimates, the second step estimates the
dependence parameters with a pairwise likelihood using block maxima. In a
simulation study, the two-step approach was found to be more efficient than the
pairwise likelihood approach using only block maxima. The method was applied to
study the effect of El Ni\~{n}o-Southern Oscillation on extreme precipitation
in California with maximum daily winter precipitation from 35 sites over 55
years. Using site-specific generalized extreme value models, the two-step
approach led to more sites detected with the El Ni\~{n}o effect, narrower
confidence intervals for return levels and tighter confidence regions for risk
measures of jointly defined events.Comment: Published at http://dx.doi.org/10.1214/14-AOAS804 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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