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Linear Programming Estimators and Bootstrapping for Heavy Tailed Phenomena
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Convergence of Probability Measures
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Extreme Values, Regular Variation and Point Processes
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Time Series: Theory and Methods
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An introduction to probability theory and its applications
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Extremes of moving averages of random variables from the domain of attraction of the double exponential distribution
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Estimation for first-order autoregressive processes with positive or bounded innovations
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Limit distributions for linear programming time series estimators
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ENCYCLOPEDIA OF MATHEMATICS AND ITS APPLICATIONS
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An Introduction to Probability Theory and its Applications
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Bootstrap Inference for a First-Order Autoregression with Positive Innovations
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Estimation for autoregressive processes with positive innovations
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Testing for independence in heavy tailed and positive innovation time series
inferredcites →https://doi.org/10.1111/j.1467-9892.1989.tb00011.xopenalex-snapshot
NON‐NEGATIVE AUTOREGRESSIVE PROCESSES
inferredcites →https://doi.org/10.1214/aop/1176990748openalex-snapshot
Asymptotic Properties of the Bootstrap for Heavy-Tailed Distributions
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Limit Theory for Moving Averages of Random Variables with Regularly Varying Tail Probabilities
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Necessary Conditions for the Bootstrap of the Mean
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On the Bootstrap of the Sample Mean in the Infinite Variance Case
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Bootstrap of the Mean in the Infinite Variance Case
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On Regular Variation and Its Application to the Weak Convergence of Sample Extremes
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Point processes, regular variation and weak convergence
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Consistency of Hill's estimator for dependent data
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Estimation and linear prediction for regression, autoregression and ARMA with infinite variance data
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Bootstrap Methods for Statistical Inference. Part II: Extreme-Value Analysis
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Heavy tail modeling and teletraffic data: special invited paper
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