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Small-sample likelihood-based inference in the Arfima model
Offer Lieberman
, Judith Rousseau
, David M. Zucker
Technion-Israel Institute of Technology
CREST-ENSAE
Hebrew University of Jerusalem
Research output
:
Contribution to journal
›
Article
›
peer-review
9
Scopus citations
Overview
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Dive into the research topics of 'Small-sample likelihood-based inference in the Arfima model'. Together they form a unique fingerprint.
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Keyphrases
ARFIMA
100%
Likelihood-free Inference
100%
Sample Likelihood
100%
Moving Average Model
66%
Simulation Study
33%
Maximum Likelihood Estimation
33%
Correction Factor
33%
Likelihood Ratio Test
33%
Bartlett Correction
33%
Small Sample Inference
33%
Fractional Difference
33%
Asymptotic Properties
33%
Degree of Approximation
33%
Long-range Dependence
33%
Conventional Test
33%
Asymptotic Order
33%
Popular
33%
Corrected Likelihood
33%
Mathematics
Smaller Sample
100%
Moving Average Model
66%
Asymptotics
33%
Approximation Order
33%
Range Dependence
33%
Simulation Study
33%
Bartlett Correction
33%
Likelihood Ratio Test
33%
Gaussian Distribution
33%
Asymptotic Property
33%
Maximum Likelihood Estimate
33%
Economics, Econometrics and Finance
ARMA Model
100%
Time Series
50%