Randomization, Bootstrap and Monte Carlo Methods in Biology

Manly, Bryan F. J.

doi:10.1201/9781315273075

Cited by 1,190 publications

(758 citation statements)

References 0 publications

Supporting

Mentioning

726

Contrasting

Unclassified

Order By: Relevance

“…We performed all analyses with randomization tests applied to Euclidean distance between sampling units (10,000 iterations). We used randomization tests because there are no assumptions regarding normal distribution of data (Manly 2007). We used the MULTIV software (Pillar 2005).…”

Section: Smoke-liquid Solution Treatment (Sls)-smoke-liquid Solu-mentioning

confidence: 99%

Does Fire Trigger Seed Germination in the Neotropical Savannas? Experimental Tests with Six Cerrado Species

et al. 2016

View full text Add to dashboard Cite

The Cerrado (Brazilian savanna) is a biodiversity hotspot with a history of fire that goes back as far as 10 million years. Fire has influenced the evolution of several aspects of the vegetation, including reproduction and life cycles. This study tested how fire by‐products such as heat and smoke affect the germination of six species common to two Cerrado open physiognomies: wet grasslands and the campo sujo (grassland with scattered shrubs and dwarf trees). We subjected seeds collected in northern Brazil to heat shock and smoke treatments, both separately and combined, using different temperatures, exposure times, and smoke concentrations in aqueous solutions. High temperatures and smoke did not break seed dormancy nor stimulate germination of the Cerrado study species. However, seeds were not killed by high temperatures, indicating that they are fire‐tolerant. Our findings differed from those of other fire‐prone ecosystems (mostly of Mediterranean vegetation), where fire stimulates germination. Moreover, we provide important information regarding germination strategies of non‐woody Cerrado plants, showing the importance of considering the tolerance of seeds to high temperatures when evaluating fire‐related traits in fire‐prone ecosystems.

show abstract

Section: Smoke-liquid Solution Treatment (Sls)-smoke-liquid Solu-mentioning

confidence: 99%

Does Fire Trigger Seed Germination in the Neotropical Savannas? Experimental Tests with Six Cerrado Species

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Thus, after calculating the differences in sd or dv between the actual distribution of points, a randomization procedure is needed to build a sampling distribution to test the statistical significance of the observed difference. The randomization tests are based on random rearrangements (resampling without replacement) of observations (locations) belonging to two groups to generate the differences between statistical indices computed in each rearrangement (Manly 2007). The randomization tests are also known as permutation tests because, with very small datasets, it is possible to find all the possible permutations between the elements of the datasets which are going to be compared.…”

Section: The Methodsmentioning

confidence: 99%

“…However, with databases of moderate or large size, it becomes nearly impossible to consider all the possible combinations of elements. Therefore, it is better to draw enough random samples instead of trying to obtain all the potential permutations (Good 2005;Manly 2007).…”

Section: The Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A method to test the significance of differences between centrographic measures of dispersion

López‐Castro

Thériault

Vandersmissen³

2018

Canadian Geographies / Géographies canadiennes

View full text Add to dashboard Cite

Centrographic measures of spatial dispersion, such as the standard distance, provide a numerical value to summarize the radial scattering of a set of points around their centre of gravity or centroid. This paper develops a procedure to test for the statistical significance of differences in dispersion between two sets of phenomena intertwined in space. The significance test is implemented using a resampling randomization procedure based on the pooled locations from both sets to estimate the sampling distribution of their differences. Repeated thousands of times, that yields empirical frequency thresholds of the sampling distributions to assess the statistical significance of the observed differences. Case studies based on residential locations of lone‐parent families and retired couples in the Quebec City Metropolitan Area illustrate the procedure. This paper shows how randomization procedures can be used to adapt classical tests to assess the statistical significance of differences between indices of spatial dispersion.

show abstract

“…The randomization method was introduced by Fisher (1935) and provides a very general and robust approach for computing the probability of obtaining some specific value for an estimator under the null hypothesis of no dependence. We refer interested readers to Noreen (1989) and Manly (1997) for extensive discussions of the randomization tests. In essence, randomization consists of reshuffling the data to destroy any dependence and then recalculating the test statistics for each reshuffling to estimate its distribution under the null hypothesis of no dependence.…”

Section: Iii1 Methodsologymentioning

confidence: 99%

Secular Mean Reversion and Long‐run Predictability of the Stock Market

Zakamulin¹

2016

Bulletin of Econ Res

View full text Add to dashboard Cite

The empirical financial literature reports evidence of mean reversion in stock prices and the absence of out‐of‐sample return predictability over horizons shorter than 10 years. Anecdotal evidence suggests the presence of mean reversion in stock prices and return predictability over horizons longer than 10 years, but thus far, there is no empirical evidence confirming such anecdotal evidence. The goal of this paper is to fill this gap in the literature. Specifically, using 141 years of data, this paper begins by performing formal tests of the random walk hypothesis in the prices of the real S&P Composite Index over increasing time horizons of up to 40 years. Although our results cannot support the conventional wisdom that the stock market is safer for long‐term investors, our findings speak in favor of the mean reversion hypothesis. In particular, we find statistically significant in‐sample evidence that past 15‐17 year returns are able to predict the future 15‐17 year returns. This finding is robust to the choice of data source, deflator, and test statistic. The paper continues by investigating the out‐of‐sample performance of long‐horizon return forecasting based on the mean‐reverting model. These latter tests demonstrate that the forecast accuracy provided by the mean‐reverting model is statistically significantly better than the forecast accuracy provided by the naive historical‐mean model. Moreover, we show that the predictive ability of the mean‐reverting model is economically significant and translates into substantial performance gains.

show abstract

Randomization, Bootstrap and Monte Carlo Methods in Biology

Cited by 1,190 publications

References 0 publications

Does Fire Trigger Seed Germination in the Neotropical Savannas? Experimental Tests with Six Cerrado Species

Does Fire Trigger Seed Germination in the Neotropical Savannas? Experimental Tests with Six Cerrado Species

A method to test the significance of differences between centrographic measures of dispersion

Secular Mean Reversion and Long‐run Predictability of the Stock Market

Contact Info

Product

Resources

About