Results 131 to 140 of about 87,023 (313)

On the Optimal Prediction of Extreme Events in Heavy‐Tailed Time Series With Applications to Solar Flare Forecasting

Journal of Time Series Analysis, EarlyView.
ABSTRACT The prediction of extreme events in time series is a fundamental problem arising in many financial, scientific, engineering, and other applications. We begin by establishing a general Neyman–Pearson‐type characterization of optimal extreme event predictors in terms of density ratios.
Victor Verma, Stilian Stoev, Yang Chen
wiley   +1 more source

Copulas and Markov operators [PDF]

, 1996
Elwood T. Olsen, William F. Darsow, Bao Nguyen   +2 more
openalex   +1 more source

Markov Determinantal Point Process for Dynamic Random Sets

Journal of Time Series Analysis, EarlyView.
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley   +1 more source

Hierarchical Ambiguities in Copula Coordinate Structures in Slovene and Other Slavic Languages [PDF]

, 1997
Donald F. Reindl
openalex   +1 more source

A new class of copulas with tail dependence and a generalized tail dependence estimator [PDF]

We present a new family of copulas (generalized mean copulas) which is positive comprehensive and allows for upper tail dependence. It includes the Spearman copula and a specific Fréchet copula as special cases.
Fischer, Matthias J., Hinzmann, Gerd
core  

Multiple Changepoint Detection for Non‐Gaussian Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios.
Robert Lund, Thomas J. Fisher, Norou Diawara, Michael Wehner   +3 more
wiley   +1 more source

A Conditional Tail Expectation Type Risk Measure for Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We consider the estimation of the conditional expectation 𝔼(Xh|X0>UX(1/p)), provided 𝔼|X0|<∞, at extreme levels, where (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$ is a strictly stationary time series, UX$$ {U}_X $$ its tail quantile function, h$$ h $$ is a positive integer and p∈(0,1)$$ p\in \left(0,1\right) $$ is such that p→0$$ p\to ...
Yuri Goegebeur, Armelle Guillou, Jing Qin   +2 more
wiley   +1 more source

Modeling Multivariate Distributions with Continuous Margins Using the copula R Package

Journal of Statistical Software, 2010
The copula-based modeling of multivariate distributions with continuous margins is presented as a succession of rank-based tests: a multivariate test of randomness followed by a test of mutual independence and a series of goodness-of-fit tests.
Ivan Kojadinovic, Jun Yan
doaj  

Copulas Approximation and New Families [PDF]

, 2000
Valdo Durrleman, Ashkan Nikeghbali, Thierry Roncalli   +2 more
openalex   +1 more source

Robust Λ$\Lambda$‐Quantiles and Extremal Distributions

Mathematical Finance, EarlyView.
ABSTRACT In this paper, we investigate the robust models for Λ$\Lambda$‐quantiles with partial information regarding the loss distribution, where Λ$\Lambda$‐quantiles extend the classical quantiles by replacing the fixed probability level with a probability/loss function Λ$\Lambda$.
Xia Han, Peng Liu
wiley   +1 more source