Extremes of Stationary Sequences with Failures

DOI10.1080/15326340600820554zbMath1138.62026OpenAlexW1998906257WikidataQ59441887 ScholiaQ59441887MaRDI QIDQ5446506

Andreia Hall, Juerg Hüsler

Publication date: 6 March 2008

Published in: Stochastic Models (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15326340600820554

zbMATH Keywords

missing observationsfailureextreme value theoryextremal indexdiscrete distributionsexceedance point processes

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Stationary stochastic processes (60G10) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (9)

On the maxima and minima of complete and incomplete samples from nonstationary random fields ⋮ The stopped clock model ⋮ Extreme values of the uniform order 1 autoregressive processes and missing observations ⋮ Some asymptotic results on extremes of incomplete samples ⋮ Extreme values of linear processes with heavy-tailed innovations and missing observations ⋮ On the max-semistable limit of maxima of stationary sequences with missing values ⋮ Tail Dependence Under Sample Failures ⋮ Extremal behaviour of a periodically controlled sequence with imputed values ⋮ On asymptotic distribution of maxima of stationary sequences subject to random failure or censoring

Cites Work

• Extremes and related properties of random sequences and processes
• On the exceedance point process for a stationary sequence
• Mixing: Properties and examples
• Extremes of sub-sampled integer-valued moving average models with heavy-tailed innovations.
• On point processes on the circle
• Maxima and exceedances of stationary Markov chains
• An extremal markovian sequence
• Calculating the extremal index for a class of stationary sequences
• Maximum term of a particular autoregressivesequence with discrete margins
• Extremes of Some Sub-Sampled Time Series
• The extremal index and clustering of high values for derived stationary sequences
• Extreme value theory for a class of discrete distributions with applications to some stochastic processes

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