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Bootstrap Algorithm (bootstrap + algorithm)
Selected AbstractsBootstrap simulations for evaluating the uncertainty associated with peaks-over-threshold estimates of extreme wind velocityENVIRONMETRICS, Issue 1 2003M. D. Pandey Abstract In the peaks-over-threshold (POT) method of extreme quantile estimation, the selection of a suitable threshold is critical to estimation accuracy. In practical applications, however, the threshold selection is not so obvious due to erratic variation of quantile estimates with minor changes in threshold. To address this issue, the article investigates the variation of quantile uncertainty (bias and variance) as a function of threshold using a semi-parametric bootstrap algorithm. Furthermore, the article compares the performance of L-moment and de Haan methods that are used for fitting the Pareto distribution to peak data. The analysis of simulated and actual U.S. wind speed data illustrates that the L-moment method can lead to almost unbiased quantile estimates for certain thresholds. A threshold corresponding to minimum standard error appears to provide reasonable estimates of wind speed extremes. It is concluded that the quantification of uncertainty associated with a quantile estimate is necessary for selecting a suitable threshold and estimating the design wind speed. For this purpose, semi-parametric bootstrap method has proved to be a simple, practical and effective tool. Copyright © 2003 John Wiley & Sons, Ltd. [source] EMPIRICAL COMPARISON OF G MATRIX TEST STATISTICS: FINDING BIOLOGICALLY RELEVANT CHANGEEVOLUTION, Issue 10 2009Brittny Calsbeek A central assumption of quantitative genetic theory is that the breeder's equation (R=GP,1S) accurately predicts the evolutionary response to selection. Recent studies highlight the fact that the additive genetic variance,covariance matrix (G) may change over time, rendering the breeder's equation incapable of predicting evolutionary change over more than a few generations. Although some consensus on whether G changes over time has been reached, multiple, often-incompatible methods for comparing G matrices are currently used. A major challenge of G matrix comparison is determining the biological relevance of observed change. Here, we develop a "selection skewers"G matrix comparison statistic that uses the breeder's equation to compare the response to selection given two G matrices while holding selection intensity constant. We present a bootstrap algorithm that determines the significance of G matrix differences using the selection skewers method, random skewers, Mantel's and Bartlett's tests, and eigenanalysis. We then compare these methods by applying the bootstrap to a dataset of laboratory populations of Tribolium castaneum. We find that the results of matrix comparison statistics are inconsistent based on differing a priori goals of each test, and that the selection skewers method is useful for identifying biologically relevant G matrix differences. [source] The effect of fixed-count subsampling on macroinvertebrate biomonitoring in small streamsFRESHWATER BIOLOGY, Issue 2 2000Craig P. Doberstein Summary 1When rigorous standards of collecting and analysing data are maintained, biological monitoring adds valuable information to water resource assessments. Decisions, from study design and field methods to laboratory procedures and data analysis, affect assessment quality. Subsampling - a laboratory procedure in which researchers count and identify a random subset of field samples - is widespread yet controversial. What are the consequences of subsampling? 2To explore this question, random subsamples were computer generated for subsample sizes ranging from 100 to 1000 individuals as compared with the results of counting whole samples. The study was done on benthic invertebrate samples collected from five Puget Sound lowland streams near Seattle, WA, USA. For each replicate subsample, values for 10 biological attributes (e.g. total number of taxa) and for the 10-metric benthic index of biological integrity (B-IBI) were computed. 3Variance of each metric and B-IBI for each subsample size was compared with variance associated with fully counted samples generated using the bootstrap algorithm. From the measures of variance, we computed the maximum number of distinguishable classes of stream condition as a function of sample size for each metric and for B-IBI. 4Subsampling significantly decreased the maximum number of distinguishable stream classes for B-IBI, from 8.2 for fully counted samples to 2.8 classes for 100-organism subsamples. For subsamples containing 100,300 individuals, discriminatory power was low enough to mislead water resource decision makers. [source] A linear benchmark for forecasting GDP growth and inflation?JOURNAL OF FORECASTING, Issue 4 2008Massimiliano MarcellinoArticle first published online: 30 APR 200 Abstract Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory-based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||