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: The likelihood ratio test is presented, potentially more sensitive than both the standard t test and its permutation‐based version, and results from the Functional Imaging Analysis Contest 2005 dataset are presented to support this claim.
Abstract: In group average analyses, we generalize the classical one-sample t test to account for heterogeneous within-subject uncertainties associated with the estimated effects. Our test statistic is defined as the maximum likelihood ratio corresponding to a Gaussian mixed-effect model. The test's significance level is calibrated using the same sign permutation framework as in Holmes et al., allowing for exact specificity control under a mild symmetry assumption about the subjects' distribution. Because our likelihood ratio test does not rely on homoscedasticity, it is potentially more sensitive than both the standard t test and its permutation-based version. We present results from the Functional Imaging Analysis Contest 2005 dataset to support this claim.
49 citations
TL;DR: The proposed method has always reached the correct ranking with less samples and, in the case of non-Gaussian PDFs, the proposed methodology has worked well, while the other methods have not been able to detect some PDF differences.
Abstract: This paper presents a statistical based comparison methodology for performing evolutionary algorithm comparison under multiple merit criteria. The analysis of each criterion is based on the progressive construction of a ranking of the algorithms under analysis, with the determination of significance levels for each ranking step. The multicriteria analysis is based on the aggregation of the different criteria rankings via a non-dominance analysis which indicates the algorithms which constitute the efficient set. In order to avoid correlation effects, a principal component analysis pre-processing is performed. Bootstrapping techniques allow the evaluation of merit criteria data with arbitrary probability distribution functions. The algorithm ranking in each criterion is built progressively, using either ANOVA or first order stochastic dominance. The resulting ranking is checked using a permutation test which detects possible inconsistencies in the ranking-leading to the execution of more algorithm runs which refine the ranking confidence. As a by-product, the permutation test also delivers -values for the ordering between each two algorithms which have adjacent rank positions. A comparison of the proposed method with other methodologies has been performed using reference probability distribution functions (PDFs). The proposed methodology has always reached the correct ranking with less samples and, in the case of non-Gaussian PDFs, the proposed methodology has worked well, while the other methods have not been able even to detect some PDF differences. The application of the proposed method is illustrated in benchmark problems.
49 citations
TL;DR: The proposed test statistic is modified and extended to factorial MANOVA designs, incorporating general heteroscedastic models, and the only distributional assumption is the existence of the group-wise covariance matrices, which may even be singular.
49 citations
TL;DR: Given a symbolization of the observed time series, the technology behind adaptive dictionary data compression algorithms offers a suitable estimate of reversibility, as well as a statistical likelihood test, which creates approximately independent segments permitting a simple and direct null test without resampling or surrogate data.
Abstract: Time symmetry, often called statistical time reversibility, in a dynamical process means that any segment of time-series output has the same probability of occurrence in the process as its time reversal. A technique, based on symbolic dynamics, is proposed to distinguish such symmetrical processes from asymmetrical ones, given a time-series observation of the otherwise unknown process. Because linear stochastic Gaussian processes, and static nonlinear transformations of them, are statistically reversible, but nonlinear dynamics such as dissipative chaos are usually statistically irreversible, a test will separate large classes of hypotheses for the data. A general-purpose and robust statistical test procedure requires adapting to arbitrary dynamics which may have significant time correlation of undetermined form. Given a symbolization of the observed time series, the technology behind adaptive dictionary data compression algorithms offers a suitable estimate of reversibility, as well as a statistical likelihood test. The data compression methods create approximately independent segments permitting a simple and direct null test without resampling or surrogate data. We demonstrate the results on various time-series-reversible and irreversible systems.
49 citations
01 Jan 2003
TL;DR: A review of the studies concerning the finite sample breakdown point (BP) of the trimmed likelihood and related estimators based on the d-fullness technique of Vandev and Neykov is made in this paper.
Abstract: A review of the studies concerning the finite sample breakdown point (BP) of the trimmed likelihood (TL) and related estimators based on the d—fullness technique of Vandev (1993), and Vandev and Neykov (1998) is made. In particular, the BP of these estimators in the frame of the generalized linear models (GLMs) depends on the trimming proportion and the quantity N(X) introduced by Muller (1995). A faster iterative algorithm based on resampling techniques for derivation of the TLE is developed. Examples of real and artificial data in the context of grouped logistic and log-linear regression models are used to illustrate the properties of the TLE.
49 citations