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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01 Jan 2002
TL;DR: Empirical likelihood for autoregressive models with inno-vations that form a martingale difference sequence is developed in this article, where the behavior of the log empirical likelihood ratio statistic is considered in nearly nonstationary models to assess the local power of unit root tests and to construct confidence intervals.
Abstract: Empirical likelihood is developed for autoregressive models with inno- vations that form a martingale difference sequence. Limiting distributions of the log empirical likelihood ratio statistic for both the stable and unstable cases are established. Behavior of the log empirical likelihood ratio statistic is considered in nearly nonstationary models to assess the local power of unit root tests and to construct confidence intervals. Resampling methods are proposed to improve the finite-sample performance of empirical likelihood statistics. This paper shows that empirical likelihood methodology compares favorably with existing methods and demonstrates its potential for time series with more general innovation structures.
67 citations
TL;DR: This work examines a computationally cheap alternative whereby the tolerance intervals are derived from asymptotic theory, and examines the performance of global tests of hetereogeneous risk employing statistics based on kernel risk surfaces, paying particular attention to the choice of smoothing parameters on test power.
Abstract: Kernel smoothing is a popular approach to estimating relative risk surfaces from data on the locations of cases and controls in geographical epidemiology. The interpretation of such surfaces is facilitated by plotting of tolerance contours which highlight areas where the risk is sufficiently high to reject the null hypothesis of unit relative risk. Previously it has been recommended that these tolerance intervals be calculated using Monte Carlo randomization tests. We examine a computationally cheap alternative whereby the tolerance intervals are derived from asymptotic theory. We also examine the performance of global tests of hetereogeneous risk employing statistics based on kernel risk surfaces, paying particular attention to the choice of smoothing parameters on test power.
67 citations
TL;DR: This work surveys the methods of ISC group analysis that have been employed in the literature, and proposes less computationally intensive nonparametric methods that can be performed at the group level (for both one- and two-sample analyses), as compared to the popular method of circularly shifting the EPI time series at the individual level.
67 citations
TL;DR: This work develops a coherent methodology for the constructuion of bootstrap prediction intervals for time series that can be modeled as linear, nonlinear or nonparametric autoregressions, and presents detailed algorithms for these different models.
67 citations
TL;DR: A new Fisher's type randomization test, which has no parametric analogue, is recommended when not all subjects may be capable of responding to treatment, and proved to be more sensitive to treatment effects than Student's t.
Abstract: Non-responders attenuate average response an inflate sample variance, reducing the power of standard parametric tests. A new Fisher's type randomization test, which has no parametric analogue, is recommended when not all subjects may be capable of responding to treatment. The new test was evaluated by Monte Carlo means and applied to drug abuse data and to virus titre data. In most trial applications the new test proved to be more sensitive to treatment effects than Student's t.
67 citations