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Parametric Bootstrap Method (parametric + bootstrap_method)

Distribution by Scientific Domains
Mathematics and Statistics60%

Selected Abstracts

Selection of evolutionary models for phylogenetic hypothesis testing using parametric methods

JOURNAL OF EVOLUTIONARY BIOLOGY, Issue 4 2001
B. C. Emerson
Recent molecular studies have incorporated the parametric bootstrap method to test a priori hypotheses when the results of molecular based phylogenies are in conflict with these hypotheses. The parametric bootstrap requires the specification of a particular substitutional model, the parameters of which will be used to generate simulated, replicate DNA sequence data sets. It has been both suggested that, (a) the method appears robust to changes in the model of evolution, and alternatively that, (b) as realistic model of DNA substitution as possible should be used to avoid false rejection of a null hypothesis. Here we empirically evaluate the effect of suboptimal substitution models when testing hypotheses of monophyly with the parametric bootstrap using data sets of mtDNA cytochrome oxidase I and II (COI and COII) sequences for Macaronesian Calathus beetles, and mitochondrial 16S rDNA and nuclear ITS2 sequences for European Timarcha beetles. Whether a particular hypothesis of monophyly is rejected or accepted appears to be highly dependent on whether the nucleotide substitution model being used is optimal. It appears that a parameter rich model is either equally or less likely to reject a hypothesis of monophyly where the optimal model is unknown. A comparison of the performance of the Kishino,Hasegawa (KH) test shows it is not as severely affected by the use of suboptimal models, and overall it appears to be a less conservative method with a higher rate of failure to reject null hypotheses. [source]

Application of the parametric bootstrap method to determine statistical errors in quantitative X-ray microanalysis of thin films

JOURNAL OF MICROSCOPY, Issue 1 2007
ALDO ARMIGLIATO
Summary We applied the parametric bootstrap to the X-ray microanalysis of Si-Ge binary alloys, in order to assess the dependence of the Ge concentrations and the local film thickness, obtained by using previously described Monte Carlo methods, on the precision of the measured intensities. We show how it is possible by this method to determine the statistical errors associated with the quantitative analysis performed in sample regions of different composition and thickness, but by conducting only one measurement. We recommend the use of the bootstrap for a broad range of applications for quantitative microanalysis to estimate the precision of the final results and to compare the performances of different methods to each other. Finally, we exploited a test based on bootstrap confidence intervals to ascertain if, for given X-ray intensities, different values of the estimated composition in two points of the sample are indicative of an actual lack of homogeneity. [source]

Small area estimation of poverty indicators

THE CANADIAN JOURNAL OF STATISTICS, Issue 3 2010
Isabel Molina
Abstract The authors propose to estimate nonlinear small area population parameters by using the empirical Bayes (best) method, based on a nested error model. They focus on poverty indicators as particular nonlinear parameters of interest, but the proposed methodology is applicable to general nonlinear parameters. They use a parametric bootstrap method to estimate the mean squared error of the empirical best estimators. They also study small sample properties of these estimators by model-based and design-based simulation studies. Results show large reductions in mean squared error relative to direct area-specific estimators and other estimators obtained by "simulated" censuses. The authors also apply the proposed method to estimate poverty incidences and poverty gaps in Spanish provinces by gender with mean squared errors estimated by the mentioned parametric bootstrap method. For the Spanish data, results show a significant reduction in coefficient of variation of the proposed empirical best estimators over direct estimators for practically all domains. The Canadian Journal of Statistics 38: 369,385; 2010 © 2010 Statistical Society of Canada Les auteurs proposent d'estimer les paramètres non linéaires d'une population de petits domaines en utilisant une méthode bayésienne empirique. L'emphase est mise sur les indicateurs de pauvreté comme paramètres non linéaires d'intérêt particuliers, mais ils proposent une méthodologie qui s'applique à des paramètres non linéaires plus généraux. Ils utilisent une méthode de rééchantillonnage paramétrique pour estimer l'erreur quadratique moyenne du meilleur estimateur empirique. À l'aide de simulations basées sur le modèle et sur le plan de sondage, ils étudient les propriétés de ces estimateurs pour les petits échantillons. Les résultats obtenus montrent une grande réduction de l'erreur quadratique moyenne par rapport aux estimateurs propres aux régions et les autres estimateurs obtenus par recensements « simulés». Les auteurs ont aussi appliqué la méthodologie proposée à l'estimation des incidences de pauvreté et des disparités, en fonction du sexe, du niveau de la pauvreté des provinces espagnoles. Les erreurs quadratiques moyennes sont estimées en utilisant la méthode de rééchantillonnage paramétrique citée auparavant. Pour les données espagnoles, les résultats montrent une réduction substantielle du coefficient de variation des meilleurs estimateurs empiriques proposés par rapport aux estimateurs spécifiques pour pratiquement tous les domaines. La revue canadienne de statistique 38: 369,385; 2010 © 2010 Société statistique du Canada [source]

An adaptive hierarchical Bayes quality measurement plan,

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2009
Partha Lahiri
Abstract The quality of a production process is often judged by a quality assurance audit, which is essentially a structured system of sampling inspection plan. The defects of sampled products are assessed and compared with a quality standard, which is determined from a tradeoff among manufacturing costs, operating costs and customer needs. In this paper, we propose a new hierarchical Bayes quality measurement plan that assumes an implicit prior for the hyperparameters. The resulting posterior means and variances are obtained adaptively using a parametric bootstrap method. Published in 2009 by John Wiley & Sons, Ltd. [source]