Bootstrap Methods (bootstrap + methods)

Distribution by Scientific Domains
Distribution within Business, Economics, Finance and Accounting


Selected Abstracts


Bootstrap Methods for Markov Processes

ECONOMETRICA, Issue 4 2003
Joel L. Horowitz
The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition density nonparametrically. Bootstrap samples are obtained by sampling the process implied by the estimated transition density. Conditions are given under which the errors made by the Markov bootstrap converge to zero more rapidly than those made by the block bootstrap. [source]


Bootstrap Methods in Econometrics*

THE ECONOMIC RECORD, Issue 2006
JAMES G. MacKINNON
There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods do not always work well. This paper discusses a large number of bootstrap methods that can be useful in econometrics. Applications to hypothesis testing are emphasized, and simulation results are presented for a few illustrative cases. [source]


Bootstrap methods for assessing the performance of near-infrared pattern classification techniques

JOURNAL OF CHEMOMETRICS, Issue 5 2002
Brandye M. Smith
Abstract Two parametric bootstrap techniques were applied to near-infrared (NIR) pattern classification models for two classes of microcrystalline cellulose, Avicel® PH101 and PH102, which differ only in particle size. The development of pattern classification models for similar substances is difficult, since their characteristic clusters overlap. Bootstrapping was used to enlarge small test sets for a better approximation of the overlapping area of these nearly identical substances, consequently resulting in better estimates of misclassification rates. A bootstrap that resampled the residuals, referred to as the outside model space bootstrap in this paper, and a novel bootstrap that resampled principal component scores, referred to as the inside model space bootstrap, were studied. A comparison revealed that classification rates for both bootstrap techniques were similar to the original test set classification rates. The bootstrap method developed in this study, which resampled the principal component scores, was more effective for estimating misclassification volumes than the residual-resampling method. Copyright © 2002 John Wiley & Sons, Ltd. [source]


Estimation of the expected ROCOF of a repairable system with bootstrap confidence region

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2001
M. J. Phillips
Abstract Bootstrap methods are presented for constructing confidence regions for the expected ROCOF of a repairable system. This is based on the work of Cowling et al. (Journal of the American Statistical Association 1996; 91: 1516,1524) for the intensity function of a NHPP. The method is applied to the failure times of a photocopier given by Baker (Technometrics 1996; 38: 256,265). Copyright © 2001 John Wiley & Sons, Ltd. [source]


Inter-ocean dispersal is an important mechanism in the zoogeography of hakes (Pisces: Merluccius spp.)

JOURNAL OF BIOGEOGRAPHY, Issue 6 2001
W. Stewart Grant
Aim To present new genetic data and to review available published genetic data that bear on the phylogeny of hakes in the genus Merluccius. To construct a zoogeographical model from a summary phylogenetic tree with dated nodes. To search for an explanation of antitropical distributions in hakes. To assess peripheral isolate, centrifugal and vicariance models of speciation in view of the molecular phylogeny and zoogeography of hakes. Locations Northern and southern Atlantic Ocean, eastern Pacific Ocean, South Pacific Ocean. Methods Electrophoretic analysis of 20 allozyme loci in 10 species of hakes. Phylogenetic tree construction with parsimony and bootstrap methods. Reanalysis of previous genetic data. Analysis of zoogeographical patterns with geographical distributions of molecular genetic markers. Results Phylogenetic analyses of new and previous allozyme data and previous mitochondrial DNA data indicate a deep genetic partition between Old- and New-World hakes with genetic distances corresponding to 10,15 Myr of separation. This time marks a widening rift between Europe and North America and a rapid drop in ocean temperatures that subdivided an ancestral population of North Atlantic hake. Two Old-World clades spanning the equator include pairs of sister taxa separated by tropical waters. Divergence times between these pairs of sister-taxa variously date to the early Pliocene and late Pleistocene. Amongst New-World hakes, pairs of sister taxa are separated by equatorial waters, by the Southern Ocean, and by the Panama Isthmus. These genetic separations reflect isolation by the rise of the Isthmus 3,4 Ma and by Pliocene and Pleistocene dispersals. Pairs of species occurring in sympatry or parapatry in six regions do not reflect sister-species relationships, but appear to reflect allopatric divergence and back dispersals of descendent species. Some geographically isolated regional populations originating within the last few hundreds of thousands of years merit subspecies designations. Conclusions Vicariance from tectonic movement of continental plates or ridge formation cannot account for the disjunct distributions of most hake sister taxa. Molecular genetic divergences place the origin of most hake species diversity in the last 2,3 Myr, a period of negligible tectonic activity. Distributions of many hake species appear to have resulted from dispersals and back dispersals across both warm equatorial waters and cool waters in the Southern Ocean, driven by oscillations in climate and ocean temperatures. Genetic and ecological divergence prevents hybridization and competitive exclusion between sympatric species pairs in six regions. Sister-taxa relationships and estimates of divergence are consistent with the modified peripheral isolate model of speciation in which vicariances, range expansions and contractions, dispersals and founder events lead to isolated populations that subsequently diverge to form new species. [source]


VARIANCE-RATIO TESTS OF RANDOM WALK: AN OVERVIEW

JOURNAL OF ECONOMIC SURVEYS, Issue 3 2009
Amélie Charles
Abstract This paper reviews the recent developments in the field of the variance-ratio (VR) tests of the random walk and martingale hypothesis. In particular, we present the conventional individual and multiple VR tests as well as their improved modifications based on power-transformed statistics, rank and sign tests, subsampling and bootstrap methods, among others. We also re-examine the weak-form efficiency for five emerging equity markets in Latin America. [source]


Bootstrapping Financial Time Series

JOURNAL OF ECONOMIC SURVEYS, Issue 3 2002
Esther Ruiz
It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate. [source]


Tilting methods for assessing the influence of components in a classifier

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 4 2009
Peter Hall
Summary., Many contemporary classifiers are constructed to provide good performance for very high dimensional data. However, an issue that is at least as important as good classification is determining which of the many potential variables provide key information for good decisions. Responding to this issue can help us to determine which aspects of the datagenerating mechanism (e.g. which genes in a genomic study) are of greatest importance in terms of distinguishing between populations. We introduce tilting methods for addressing this problem. We apply weights to the components of data vectors, rather than to the data vectors themselves (as is commonly the case in related work). In addition we tilt in a way that is governed by L2 -distance between weight vectors, rather than by the more commonly used Kullback,Leibler distance. It is shown that this approach, together with the added constraint that the weights should be non-negative, produces an algorithm which eliminates vector components that have little influence on the classification decision. In particular, use of the L2 -distance in this problem produces properties that are reminiscent of those that arise when L1 -penalties are employed to eliminate explanatory variables in very high dimensional prediction problems, e.g. those involving the lasso. We introduce techniques that can be implemented very rapidly, and we show how to use bootstrap methods to assess the accuracy of our variable ranking and variable elimination procedures. [source]


Bootstrap predictive inference for ARIMA processes

JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2004
Lorenzo Pascual
Abstract., In this study, we propose a new bootstrap strategy to obtain prediction intervals for autoregressive integrated moving-average processes. Its main advantage over other bootstrap methods previously proposed for autoregressive integrated processes is that variability due to parameter estimation can be incorporated into prediction intervals without requiring the backward representation of the process. Consequently, the procedure is very flexible and can be extended to processes even if their backward representation is not available. Furthermore, its implementation is very simple. The asymptotic properties of the bootstrap prediction densities are obtained. Extensive finite-sample Monte Carlo experiments are carried out to compare the performance of the proposed strategy vs. alternative procedures. The behaviour of our proposal equals or outperforms the alternatives in most of the cases. Furthermore, our bootstrap strategy is also applied for the first time to obtain the prediction density of processes with moving-average components. [source]


Improving robust model selection tests for dynamic models

THE ECONOMETRICS JOURNAL, Issue 2 2010
Hwan-sik Choi
Summary, We propose an improved model selection test for dynamic models using a new asymptotic approximation to the sampling distribution of a new test statistic. The model selection test is applicable to dynamic models with very general selection criteria and estimation methods. Since our test statistic does not assume the exact form of a true model, the test is essentially non-parametric once competing models are estimated. For the unknown serial correlation in data, we use a Heteroscedasticity/Autocorrelation-Consistent (HAC) variance estimator, and the sampling distribution of the test statistic is approximated by the fixed- b,asymptotic approximation. The asymptotic approximation depends on kernel functions and bandwidth parameters used in HAC estimators. We compare the finite sample performance of the new test with the bootstrap methods as well as with the standard normal approximations, and show that the fixed- b,asymptotics and the bootstrap methods are markedly superior to the standard normal approximation for a moderate sample size for time series data. An empirical application for foreign exchange rate forecasting models is presented, and the result shows the normal approximation to the distribution of the test statistic considered appears to overstate the data's ability to distinguish between two competing models. [source]


Bootstrap Methods in Econometrics*

THE ECONOMIC RECORD, Issue 2006
JAMES G. MacKINNON
There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods do not always work well. This paper discusses a large number of bootstrap methods that can be useful in econometrics. Applications to hypothesis testing are emphasized, and simulation results are presented for a few illustrative cases. [source]


Youden Index and Optimal Cut-Point Estimated from Observations Affected by a Lower Limit of Detection

BIOMETRICAL JOURNAL, Issue 3 2008
Marcus D. Ruopp
Abstract The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]


Regional Climate Change: Trend Analysis of Temperature and Precipitation Series at Selected Canadian Sites

CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS, Issue 1 2000
J. Stephen Clark
Global climate change does not necessarily imply that temperature or precipitation is increasing at specific locations. The hypothesis of increasing temperature and precipitation trends associated with global climate change is tested using actual annual temperature and precipitation data for nine selected weather stations, spatially distributed across Canada. Vogelsang's (1998) partial sum and Woodward et al's (1997) bootstrap methods are used for testing for trend. Both methods suggest no warming in the Canadian temperature series except for Toronto, Ontario, which had significant increase over time, along with Moncton, New Brunswick, and Indian Head, Saskatchewan, which had marginal increases. There is no evidence of increasing trend in precipitation except for Moncton, New Brunswick, which had a significantly increasing trend. Thus, public policies designed to address the regional effects of climate change need to be adapted for a particular ecological zone, based on knowledge of the climate trends for that region, rather than on general global climate change patterns. Les changements climatiques à l'échelle planétaire ne signifient pas nécessairement que la température et les précipitations sont en augmentation dans des emplacements donnés. Nous avons testé I'hypothèse d'une assoviation de la tendance à la hausse de la température et des précipitations avec les changements climatiques planétaires à partir des données réelles de température et de précipitations obtenues à 9 stations d'observation climatique réparties dans les diverses régions du Canada. Nous utilisons, pour cefaire, la méthode des sommes partielles de Vogelsang (1998) et celle de rééchantillonnage bootstrap de Woodward et al (1997). Les deux méthodes ne révèlent aucun réchauffement de la température dans les séries chronologiques, sauf pour Toronto, en Ontario, où l'on constate une hausse significative en fonction du temps, ainsi que pour Moncton au Nouveau-Brunswick et Indian Head en Saskatchewan qui marquent de très légères augmentations. Rien n'indique une tendance à la hausse des précipitations, sauf à Moncton où se dessine une tendance significative dans ce sens. Les programmes publics destinés à faire face aux effets régionaux du changement climatique doivent donc être adaptés à chaque zone écologique particulière, à partir d"observations faites dans la région même, plutôt que de la configuration du changement climatique à l'échelle planétaire. [source]