Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.

Vector Autoregressions and Reality

Abstract: This article questions the statistical significance of variance decompositions and impulse response functions for unrestricted vector autoregressions. It suggests that previous authors have failed to provide confidence intervals for variance decompositions and impulse response functions. Two methods of computing such confidence intervals are developed: first, using a normal approximation; second, using bootstrapped resampling. An example from Sims's work is used to illustrate the importance of computing these confidence intervals. In this example, the 95% confidence intervals for variance decompositions span up to 66 percentage points at the usual forecasting horizon.

An E-M algorithm and testing strategy for multiple-locus haplotypes.

Abstract: This paper gives an expectation maximization (EM) algorithm to obtain allele frequencies, haplotype frequencies, and gametic disequilibrium coefficients for multiple-locus systems. It permits high polymorphism and null alleles at all loci. This approach effectively deals with the primary estimation problems associated with such systems; that is, there is not a one-to-one correspondence between phenotypic and genotypic categories, and sample sizes tend to be much smaller than the number of phenotypic categories. The EM method provides maximum-likelihood estimates and therefore allows hypothesis tests using likelihood ratio statistics that have chi 2 distributions with large sample sizes. We also suggest a data resampling approach to estimate test statistic sampling distributions. The resampling approach is more computer intensive, but it is applicable to all sample sizes. A strategy to test hypotheses about aggregate groups of gametic disequilibrium coefficients is recommended. This strategy minimizes the number of necessary hypothesis tests while at the same time describing the structure of disequilibrium. These methods are applied to three unlinked dinucleotide repeat loci in Navajo Indians and to three linked HLA loci in Gila River (Pima) Indians. The likelihood functions of both data sets are shown to be maximized by the EM estimates, and the testing strategy provides a useful description of the structure of gametic disequilibrium. Following these applications, a number of simulation experiments are performed to test how well the likelihood-ratio statistic distributions are approximated by chi 2 distributions. In most circumstances the chi 2 grossly underestimated the probability of type I errors. However, at times they also overestimated the type 1 error probability. Accordingly, we recommended hypothesis tests that use the resampling method.

Resampling tests for meta-analysis of ecological data

Abstract: Meta-analysis is a statistical technique that allows one to combine the results from multiple studies to glean inferences on the overall importance of various phenomena. This method can prove to be more informative than common ''vote counting,'' in which the number of significant results is compared to the number with nonsignificant results to determine whether the phenomenon of interest is globally important. While the use of meta- analysis is widespread in medicine and the social sciences, only recently has it been applied to ecological questions. We compared the results of parametric confidence limits and ho- mogeneity statistics commonly obtained through meta-analysis to those obtained from re- sampling methods to ascertain the robustness of standard meta-analytic techniques. We found that confidence limits based on bootstrapping methods were wider than standard confidence limits, implying that resampling estimates are more conservative. In addition, we found that significance tests based on homogeneity statistics differed occasionally from results of randomization tests, implying that inferences based solely on chi-square signif- icance tests may lead to erroneous conclusions. We conclude that resampling methods should be incorporated in meta-analysis studies, to ensure proper evaluation of main effects in ecological studies.

Computational Methods for Measuring the Difference of Empirical Distributions

Abstract: This paper presents a simple computational method for measuring the difference of independent empirical distributions estimated by bootstrapping or other resampling approaches. Using data from a field test of external scope in contingent valuation, this complete combinatorial method is compared with other methods (empirical convolutions, repeated sampling, normality, nonoverlapping confidence intervals) that have been suggested in the literature. Tradeoffs between methods are discussed in terms of programming complexity, time and computer resources required, bias, and the precision of the estimate.