Bootstrap Methods for Markov Processes

By Joel L. Horowitz. 2003

137 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e4230628b01c09b4ffe6bf1c76aada65b5d8ddee

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Citations in the corpus

Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains (2010, methodology): “In particular, the Markov bootstrap has been shown to have a lower coverage error for Markov data than the block bootstrap under certain restricted conditions [10].”
The bootstrap in econometrics (2003, methodology): “Horowitz (2003) gives conditions under which the errors in Markov bootstrap estimates of onesided and symmetrical distribution functions and the ERPs of one-sided and symmetrical hypothesis tests are O(n−1+ε) and O(n−3/2+ε), respectively, for any ε > 0.”
Bandwidth Selection in Nonparametric Kernel Testing (2008, background): “KEY WORDS: Choice of bandwidth parameter; Edgeworth expansion; Nonparametric kernel testing; Power function; Size function.”
TESTING FOR THE MARKOV PROPERTY IN TIME SERIES (2011, methodology): “To overcome such adverse impact, we will use Horowitz’s (2003) nonparametric smoothed transition density-based bootstrap procedure to obtain the critical values of the test in finite samples.”
Bootstrapping the Box–Pierce Q test: A robust test of uncorrelatedness (2006, methodology): “Note that for reasons explained by Horowitz and Savin (2000), the P-values of the K Q ~ , QK*, GPK tests and the DBOB QK test are not corrected to be exactly 0.”
Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form (2005, background): “Horowitz (2003) imposed a similar condition in a different context.”
The Impact of Bootstrap Methods on Time Series Analysis (2003, methodology): “In the case of dependent stationary data, under some regularity conditions, the appropriately standardized/Studentized block bootstrap (in all its variations, including the stationary and circular bootstrap) also possesses a “higher-order accuracy” property when estimating the distribution of the sa”
Bootstrap Methods in Econometrics (2018, unknown): “Horowitz (2003) investigates the ability of the MB to provide asymptotic refinements for confidence intervals and tests based on Studentized statistics of the form 1/2[ ( ) ( )] /n Tt T H m H sµ= − , where H is a smooth function, 1( )E Xµ = , 1 1 T t t m T X− = = ∑ , and 2Ts is a consistent…”
The Block-Block Bootstrap: Improved Asymptotic Refinements (2002, background): “Under some conditions, the MCB yields asymptotic refinements that exceed those of the block bootstrap; see Horowitz (2003).”
Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes (2008, unknown): “…of the block bootstrap has led to the search for other ways to implement the bootstrap with dependent data and to the development of adaptations designed to increase the asymptotic refinement of the block bootstrap (see, e.g. Horowitz, 2003; Andrews, 2004, and the references contained therein).”
Generalized Method of Moments and Macroeconomics (2002, methodology): “Horowitz (2002) showed that in some cases the MCB achieves a better asymptotic re nement than the block bootstrap.”