Monte Carlo Methods

Manly, Bryan F. J.; Alberto, Jorge

doi:10.1201/9780429329203-3

Cited by 34 publications

(43 citation statements)

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“…Fourth, while the intuition of permutation testing fits most naturally to hypothesis testing, the methodology can also be used to generate a form of confidence interval (Manly, 2007 : pp. 18–20; Miller, 1997 : pp.…”

Section: Permutation Tests For K = 2 Independent S...mentioning

confidence: 99%

“…In fact, while many permutation strategies have been suggested for the multiple regression context, there remains no generally accepted permutation approach for this problem. A full survey of this literature is beyond the scope of discussion, but helpful commentaries are provided by Kennedy ( 1995 ), Kennedy and Cade ( 1996 ), Manly ( 2007 : ch. 8), Anderson and Robinson ( 2001 ), and Winkler et al ( 2014 ).…”

Section: Permutation Tests For Linear Relationshipsmentioning

confidence: 99%

“…The methodology of permutation testing will be familiar to those who have experience with bootstrap tests (Efron & Tibshirani, 1993 ), but with one important difference: whereas the theory behind bootstrap inference is based on random resampling with replacement from the observed data, the theory behind permutation inference is based on exhaustive permutation of the observed data or random sampling without replacement . Manly ( 2007 ) discusses the implementation details and strengths of each approach. For our purposes, it is enough to note that permutation methods work well for small samples while the attractive properties of bootstrap methods are usually asymptotic.…”

Section: Introductionmentioning

confidence: 99%

“… 4 As for most topics in statistics, R. A. Fisher also has strong claim to name recognition for this approach (e.g., Fisher 1936 ). Manly ( 2007 : p. 113) provides an interesting discussion of philosophical contrasts between Pitman’s and Fisher’s permutation arguments. Somewhat a reflection of the weight of each scholar’s work, but mainly for expositional clarity, we refer to permutation testing in the two-independent-sample context as a Pitman permutation test, and to permutation testing in the matched-sample context as a Fisher permutation test.…”

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confidence: 99%

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Permutation tests for experimental data

Holt

Sullivan

2023

Exp Econ

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This article surveys the use of nonparametric permutation tests for analyzing experimental data. The permutation approach, which involves randomizing or permuting features of the observed data, is a flexible way to draw statistical inferences in common experimental settings. It is particularly valuable when few independent observations are available, a frequent occurrence in controlled experiments in economics and other social sciences. The permutation method constitutes a comprehensive approach to statistical inference. In two-treatment testing, permutation concepts underlie popular rank-based tests, like the Wilcoxon and Mann–Whitney tests. But permutation reasoning is not limited to ordinal contexts. Analogous tests can be constructed from the permutation of measured observations—as opposed to rank-transformed observations—and we argue that these tests should often be preferred. Permutation tests can also be used with multiple treatments, with ordered hypothesized effects, and with complex data-structures, such as hypothesis testing in the presence of nuisance variables. Drawing examples from the experimental economics literature, we illustrate how permutation testing solves common challenges. Our aim is to help experimenters move beyond the handful of overused tests in play today and to instead see permutation testing as a flexible framework for statistical inference.

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Section: Permutation Tests For K = 2 Independent S...mentioning

confidence: 99%

Section: Permutation Tests For Linear Relationshipsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Permutation tests for experimental data

Holt

Sullivan

2023

Exp Econ

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“…To provide a robust comparison among different experiments, a paired permutation test was applied at 95% confidence level with 1000 replicates (Manly 2006). Procedures in Wang and Bishop (2005) were followed to collect independent samples for the significance test.…”

Section: Experiments Designmentioning

confidence: 99%

A Simultaneous Multiscale Data Assimilation Using Scale-Dependent Localization in GSI-Based Hybrid 4DEnVar for NCEP FV3-Based GFS

Huang

Wang

Kleist³

et al. 2021

Monthly Weather Review

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A scale-dependent localization (SDL) method was formulated and implemented in the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (4DEnVar) system for NCEP FV3-based Global Forecast System (GFS). SDL applies different localization to different scales of ensemble covariances, while performing a single-step simultaneous assimilation of all available observations. Two SDL variants with (SDL-Cross) and without (SDL-NoCross) considering cross-wave-band covariances were examined. The performance of two- and three-wave-band SDL experiments (W2 and W3, respectively) was evaluated through 1-month cycled data assimilation experiments. SDL improves global forecasts to 5 days over scale-invariant localization including the operationally tuned level-dependent scale-invariant localization (W1-Ope). The W3 SDL-Cross experiment shows more accurate tropical storm–track forecasts at shorter lead times than W1-Ope. Compared to the W2 SDL experiments, the W3 SDL counterparts applying tighter horizontal localization at medium-scale wave band generally show improved global forecasts below 100 hPa, but degraded global forecasts above 50 hPa. While the outperformance of the W3 SDL-NoCross experiment versus the W2 SDL-NoCross experiment below 100 hPa lasts for 5 days, that of the W3 SDL-Cross experiment versus the W2 SDL-Cross experiment lasts for 3 days. Due to local spatial averaging of ensemble covariances that may alleviate sampling error, the SDL-NoCross experiments show slightly better forecasts than the SDL-Cross experiments at shorter lead times. However, the SDL-Cross experiments outperform the SDL-NoCross experiments at longer lead times, likely from retention of more heterogeneity of ensemble covariances and resultant analyses with improved balance. Relative performance of tropical storm–track forecasts in the W2 and W3 SDL experiments are generally consistent with that of global forecasts.

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Harmonized multi‐metric and multi‐centric assessment of EEG source space connectivity for dementia characterization

Prado,

Mejía,

Sainz‐Ballesteros

et al. 2023

Alz & Dem Diag Ass & Dis Mo

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IntroductionHarmonization protocols that address batch effects and cross‐site methodological differences in multi‐center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers.MethodsWe implemented an automatic processing pipeline incorporating electrode layout integrations, patient–control normalizations, and multi‐metric EEG source space connectomics analyses.ResultsSpline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z‐score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long‐range connections, and diminished short‐range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia.DiscussionHarmonized multi‐metric analysis of EEG source space connectivity can address data heterogeneities in multi‐centric studies, representing a powerful tool for accurately characterizing dementia.

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Monte Carlo Methods

Cited by 34 publications

References 0 publications

Permutation tests for experimental data

Permutation tests for experimental data

A Simultaneous Multiscale Data Assimilation Using Scale-Dependent Localization in GSI-Based Hybrid 4DEnVar for NCEP FV3-Based GFS

Harmonized multi‐metric and multi‐centric assessment of EEG source space connectivity for dementia characterization

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