Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
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TL;DR: In this article, a block resampling technique for strong mixing random fields is formulated, which is an extension of the "blocks of blocks" method for sequences in Politis and Romano (1992, Ann. Statist.17 1217-1241) and Liu ane Singh (1992), in Exploring Limits of Bootstrap (R. Le Page and L. Billard, Eds.), pp. 225-248, New York) have recently introduced a block re-sampling method that is successful in deriving consistent bootstrap estimates of distribution and variance for
65 citations
TL;DR: In this paper, a particle filter with Gaussian resampling (PFGR) method is proposed to generate the posterior analysis ensemble in an effective and efficient way, which can approximate more accurately the Bayesian analysis.
Abstract: Particle filter (PF) is a fully non-linear filter with Bayesian conditional probability estimation, compared here with the well-known ensemble Kalman filter (EnKF). A Gaussian resampling (GR) method is proposed to generate the posterior analysis ensemble in an effective and efficient way. The Lorenz model is used to test the proposed method. The PF with Gaussian resampling (PFGR) can approximate more accurately the Bayesian analysis. The present work demonstrates that the proposed PFGR possesses good stability and accuracy and is potentially applicable to large-scale data assimilation problems.
65 citations
TL;DR: A strategy for applying the bootstrap method to conduct either a bootstrap component or a factor analysis with a program syntax for SPSS with the Holzinger–Swineford data set is described.
Abstract: The bootstrap method, which empirically estimates the sampling distribution for either inferential or descriptive sstatistical purposes, can be applied to the multivariate case. When conducting bootstrap component, or factor, analysis, resampling results must be located in a common factor space before summary statistics for each estimated parameter can be computed. The present article describes a strategy for applying the bootstrap method to conduct either a bootstrap component or a factor analysis with a program syntax for SPSS. The Holzinger–Swineford data set is employed to make the discussion more concrete.
65 citations
TL;DR: In this article, it was shown that studentizing the sample correlation leads to a permutation test which is exact under independence and asymptotically controls the probability of Type 1 (or Type 3) errors.
Abstract: Given a sample from a bivariate distribution, consider the problem of testing independence. A permutation test based on the sample correlation is known to be an exact level α test. However, when used to test the null hypothesis that the samples are uncorrelated, the permutation test can have rejection probability that is far from the nominal level. Further, the permutation test can have a large Type 3 (directional) error rate, whereby there can be a large probability that the permutation test rejects because the sample correlation is a large positive value, when in fact the true correlation is negative. It will be shown that studentizing the sample correlation leads to a permutation test which is exact under independence and asymptotically controls the probability of Type 1 (or Type 3) errors. These conclusions are based on our results describing the almost sure limiting behavior of the randomization distribution. We will also present asymptotically robust randomization tests for regression coeffi...
64 citations
TL;DR: In this article, a specific bootstrap method is introduced for positive recurrent Markov chains, based on the regenerative method and the Nummelin splitting technique, which mainly consists in dividing the sample path of the chain into data blocks corresponding to the successive visits to the atom and resampling the blocks until the (random) length of the reconstructed trajectory is at least n.
Abstract: A specific bootstrap method is introduced for positive recurrent Markov chains, based on the regenerative method and the Nummelin splitting technique. This construction involves generating a sequence of approximate pseudo-renewal times for a Harris chain X from data X1,..., Xn and the parameters of a minorization condition satisfied by its transition probability kernel and then applying a variant of the methodology proposed by Datta and McCormick for bootstrapping additive functionals of type n-1∑i=1nf(Xi) when the chain possesses an atom. This novel methodology mainly consists in dividing the sample path of the chain into data blocks corresponding to the successive visits to the atom and resampling the blocks until the (random) length of the reconstructed trajectory is at least n, so as to mimic the renewal structure of the chain. In the atomic case we prove that our method inherits the accuracy of the bootstrap in the independent and identically distributed case up to OP(n-1) under weak conditions. In the general (not necessarily stationary) case asymptotic validity for this resampling procedure is established, provided that a consistent estimator of the transition kernel may be computed. The second-order validity is obtained in the stationary case (up to a rate close to OP(n-1) for regular stationary chains). A data-driven method for choosing the parameters of the minorization condition is proposed and applications to specific Markovian models are discussed.
64 citations