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Statistics for near independence in multivariate extreme values
We propose a multivariate extreme value threshold model for joint tail estimation which overcomes the problems encountered with existing techniques when the variables are near independence. WeExpand
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Modelling Dependence within Joint Tail Regions
Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a particular form of joint tail dependence. We develop joint tailExpand
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A new class of models for bivariate joint tails
A fundamental issue in applied multivariate extreme value analysis is modelling dependence within joint tail regions. The primary focus of this work is to extend the classical pseudopolar treatmentExpand
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Model-based geostatistics. Discussion. Authors' reply
Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S(x) based on observations (Y i = S(x i ;)+Expand
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Diagnostics for dependence within time series extremes
Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools forExpand
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Concomitant tail behaviour for extremes
The influence of bivariate extremal dependence on the limiting behaviour of the concomitant of the largest order statistic is examined. Our approach is to fix the marginal distributions and derive aExpand
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Model-based geostatistics - Discussion
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Dynamic modelling and prediction of English Football League matches for betting
Summary. We focus on modelling the 92 soccer teams in the English Football Association League over the years 1992–1997 using refinements of the independent Poisson model of Dixon and Coles. OurExpand
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An Alternative Point Process Framework for Modeling Multivariate Extreme Values
An alternative limiting point process to that of de Haan (1985) is studied that holds regardless of whether the underlying data generation mechanism is asymptotically dependent or asymptoticallyExpand
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Mathematical models of growth in stature throughout childhood.
Growth models for predicting child stature are useful to summarize both the pattern and timing of growth in individuals and populations. Jolicoeur el al. described the JPPS model and compared it withExpand
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