2,751 research outputs found
On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions
Full likelihood-based inference for high-dimensional multivariate extreme
value distributions, or max-stable processes, is feasible when incorporating
occurrence times of the maxima; without this information, -dimensional
likelihood inference is usually precluded due to the large number of terms in
the likelihood. However, some studies have noted bias when performing
high-dimensional inference that incorporates such event information,
particularly when dependence is weak. We elucidate this phenomenon, showing
that for unbiased inference in moderate dimensions, dimension should be of
a magnitude smaller than the square root of the number of vectors over which
one takes the componentwise maximum. A bias reduction technique is suggested
and illustrated on the extreme value logistic model.Comment: 7 page
Failure environment analysis tool applications
Understanding risks and avoiding failure are daily concerns for the women and men of NASA. Although NASA's mission propels us to push the limits of technology, and though the risks are considerable, the NASA community has instilled within, the determination to preserve the integrity of the systems upon which our mission and, our employees lives and well-being depend. One of the ways this is being done is by expanding and improving the tools used to perform risk assessment. The Failure Environment Analysis Tool (FEAT) was developed to help engineers and analysts more thoroughly and reliably conduct risk assessment and failure analysis. FEAT accomplishes this by providing answers to questions regarding what might have caused a particular failure; or, conversely, what effect the occurrence of a failure might have on an entire system. Additionally, FEAT can determine what common causes could have resulted in other combinations of failures. FEAT will even help determine the vulnerability of a system to failures, in light of reduced capability. FEAT also is useful in training personnel who must develop an understanding of particular systems. FEAT facilitates training on system behavior, by providing an automated environment in which to conduct 'what-if' evaluation. These types of analyses make FEAT a valuable tool for engineers and operations personnel in the design, analysis, and operation of NASA space systems
Accounting for choice of measurement scale in extreme value modeling
We investigate the effect that the choice of measurement scale has upon
inference and extrapolation in extreme value analysis. Separate analyses of
variables from a single process on scales which are linked by a nonlinear
transformation may lead to discrepant conclusions concerning the tail behavior
of the process. We propose the use of a Box--Cox power transformation
incorporated as part of the inference procedure to account parametrically for
the uncertainty surrounding the scale of extrapolation. This has the additional
feature of increasing the rate of convergence of the distribution tails to an
extreme value form in certain cases and thus reducing bias in the model
estimation. Inference without reparameterization is practicably infeasible, so
we explore a reparameterization which exploits the asymptotic theory of
normalizing constants required for nondegenerate limit distributions. Inference
is carried out in a Bayesian setting, an advantage of this being the
availability of posterior predictive return levels. The methodology is
illustrated on both simulated data and significant wave height data from the
North Sea.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS333 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Determining the Dependence Structure of Multivariate Extremes
In multivariate extreme value analysis, the nature of the extremal dependence
between variables should be considered when selecting appropriate statistical
models. Interest often lies with determining which subsets of variables can
take their largest values simultaneously, while the others are of smaller
order. Our approach to this problem exploits hidden regular variation
properties on a collection of non-standard cones and provides a new set of
indices that reveal aspects of the extremal dependence structure not available
through existing measures of dependence. We derive theoretical properties of
these indices, demonstrate their value through a series of examples, and
develop methods of inference that also estimate the proportion of extremal mass
associated with each cone. We apply the methods to UK river flows, estimating
the probabilities of different subsets of sites being large simultaneously
The Role of Worker Flows in the Dynamics and Distribution of UK Unemployment
Unemployment varies substantially over time and across subgroups of the labour market. Worker flows among labour market states act as key determinants of this variation. We examine how the structure of unemployment across groups and its cyclical movements across time are shaped by changes in labour market flows. Using novel estimates of flow transition rates for the UK over the last 35 years, we decompose unemployment variation into parts accounted for by changes in rates of job loss, job finding and flows via non-participation. Close to two-thirds of the volatility of unemployment in the UK over this period can be traced to rises in rates of job loss that accompany recessions. The share of this inflow contribution has been broadly the same in each of the past three recessions. Decreased jobfinding rates account for around one-quarter of unemployment cyclicality and the remaining variation can be attributed to flows via non-participation. Digging deeper into the structure of unemployment by gender, age and education, the flow-approach is shown to provide a richer understanding of the unemployment experiences across population subgroups.labour market, unemployment, worker flows
Higher-dimensional spatial extremes via single-site conditioning
Currently available models for spatial extremes suffer either from
inflexibility in the dependence structures that they can capture, lack of
scalability to high dimensions, or in most cases, both of these. We present an
approach to spatial extreme value theory based on the conditional multivariate
extreme value model, whereby the limit theory is formed through conditioning
upon the value at a particular site being extreme. The ensuing methodology
allows for a flexible class of dependence structures, as well as models that
can be fitted in high dimensions. To overcome issues of conditioning on a
single site, we suggest a joint inference scheme based on all observation
locations, and implement an importance sampling algorithm to provide spatial
realizations and estimates of quantities conditioning upon the process being
extreme at any of one of an arbitrary set of locations. The modelling approach
is applied to Australian summer temperature extremes, permitting assessment the
spatial extent of high temperature events over the continent
LXIV. Description of a very sensitive form of Thomson galvanometer, and some methods of galvanometer construction
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Efficient inference for spatial extreme value processes associated to log-Gaussian random functions
Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, inference for max-stable processes has been restricted to the use of pairwise composite likelihoods, due to intractability of higher-dimensional distributions. In this work we consider random fields that are in the domain of attraction of a widely used class of max-stable processes, namely those constructed via manipulation of log-Gaussian random functions. For this class, we exploit limiting d-dimensional multivariate Poisson process intensities of the underlying process for inference on all d-vectors exceeding a high marginal threshold in at least one component, employing a censoring scheme to incorporate information below the marginal threshold. We also consider the d-dimensional distributions for the equivalent max-stable process, and perform full likelihood inference by exploiting the methods of Stephenson & Tawn (2005), where information on the occurrence times of extreme events is shown to dramatically simplify the likelihood. The Stephenson-Tawn likelihood is in fact simply a special case of the censored Poisson process likelihood. We assess the improvements in inference from both methods over pairwise likelihood methodology by simulatio
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