Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, a nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series, which is based on weak assumptions on the dependence structure of the stationary process driving the random walk.
Abstract: A nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo-series retaining the important characteristics of the data. It is more general than previous bootstrap approaches to the unit root problem in that it allows for a very wide class of weakly dependent processes and it is not based on any parametric assumption on the process generating the data. As a consequence the procedure can accurately capture the distribution of many unit root test statistics proposed in the literature. Large sample theory is developed and the asymptotic validity of the block bootstrap-based unit root testing is shown via a bootstrap functional limit theorem. Applications to some particular test statistics of the unit root hypothesis, i.e., least squares and Dickey-Fuller type statistics are given. The power properties of our procedure are investigated and compared to those of alternative bootstrap approaches to carry out the unit root test. Some simulations examine the finite sample performance of our procedure.
149 citations
TL;DR: This article used the Gibbs sampling approach in the context of a three state Markov-switching model to show how heteroskedasticity affects inference and suggest two strategies for valid inference.
149 citations
Book•
30 Mar 2004
TL;DR: In this paper, Probability and Statistical Models, returns, and returns of time series models are used to estimate the value at risk of a portfolio with respect to a fixed-income portfolio.
Abstract: Introduction.- Probability and Statistical Models.- Returns.- Time Series Models.- Portfolio Theory.- Regression.- The Capital Asset Pricing Model.- Options Pricing.- Fixed Income Securities.- Resampling.- Value-at-Risk.- GARCH models.- Nonparametric Regression and Splines.- Behavioral Finance.
148 citations
TL;DR: The parametric bootstrap has made a fundamental impact on how we carry out statistical inference in problems without analytic solutions as mentioned in this paper, and this fact is illustrated with examples and comments that emphasize the parametric Bootstrap and hypothesis testing.
Abstract: The bootstrap has made a fundamental impact on how we carry out statistical inference in problems without analytic solutions. This fact is illustrated with examples and comments that emphasize the parametric bootstrap and hypothesis testing.
147 citations
TL;DR: A test of the hypothesis H-sub-0 that two sampled distributions are identical, which is assumed that two independent datasets are drawn from the respective populations, which may be very general.
Abstract: Motivated by applications in high-dimensional settings, we suggest a test of the hypothesis H-0 that two sampled distributions are identical. It is assumed that two independent datasets are drawn from the respective populations, which may be very general. In particular, the distributions may be multivariate or infinite-dimensional, in the latter case representing, for example, the distributions of random functions from one Euclidean space to another. Our test uses a measure of distance between data. This measure should be symmetric but need not satisfy the triangle inequality, so it is not essential that it be a metric. The test is based on ranking the pooled dataset, with respect to the distance and relative to any fixed data value, and repeating this operation for each fixed datum. A permutation argument enables a critical point to be chosen such that the test has concisely known significance level, conditional on the set of all pairwise distances.
147 citations