A variable-length block bootstrap method for multi-site synthetic streamflow generation

2011 ◽  
Vol 56 (3) ◽  
pp. 362-379 ◽  
Author(s):  
John Ndiritu
Keyword(s):  
Bootstrap Method ◽  
Variable Length ◽  
Block Bootstrap ◽  
Streamflow Generation ◽  
Synthetic Streamflow

scholarly journals Models for Expected Returns with Statistical Factors

2020 ◽  
Vol 13 (12) ◽  
pp. 314
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Stock Prices ◽  
Bootstrap Method ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Two Factors ◽  
Resampling Procedure

In this paper, we propose multifactor models for the pan-European Equity Market using a block-bootstrap method and compare the results with those of traditional inferential techniques. The new factors are built from statistical measurements on stock prices—in particular, coefficient of variation, skewness, and kurtosis. Data come from Reuters, correspond to nearly 2000 EU companies, and span from January 2008 to February 2018. Regarding methodology, we propose a non-parametric resampling procedure that accounts for time dependency in order to test the validity of the model and the significance of the parameters involved. We compare our bootstrap-based inferential results with classical proposals (based on F-statistics). Methods under assessment are time-series regression, cross-sectional regression, and the Fama–MacBeth procedure. The main findings indicate that the two factors that better improve the Capital Asset Pricing Model with regard to the adjusted R2 in the time-series regressions are the skewness and the coefficient of variation. For this reason, a model including those two factors together with the market is thoroughly studied. We also observe that our block-bootstrap methodology seems to be more conservative with the null of the GRS test than classical procedures.

scholarly journals Investigating Mortality Uncertainty Using the Block Bootstrap

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoming Liu ◽  
W. John Braun
Keyword(s):  
Life Expectancy ◽  
Mortality Risk ◽  
Spatial Dependence ◽  
Bootstrap Method ◽  
Prediction Intervals ◽  
Block Bootstrap ◽  
Model Framework ◽  
Model Risk ◽  
Process Risk ◽  
Carter Model

This paper proposes a block bootstrap method for measuring mortality risk under the Lee-Carter model framework. In order to take account of all sources of risk (the process risk, the parameter risk, and the model risk) properly, a block bootstrap is needed to cope with the spatial dependence found in the residuals. As a result, the prediction intervals we obtain for life expectancy are more accurate than the ones obtained from other similar methods.

Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap

2018 ◽  
Vol 2672 (42) ◽  
pp. 137-147 ◽  
Author(s):  
Bhaven Naik ◽  
Laurence R. Rilett ◽  
Justice Appiah ◽  
Lubinda F. Walubita
Keyword(s):  
Travel Time ◽  
Bootstrap Method ◽  
Point Estimate ◽  
Data Sets ◽  
Block Bootstrap ◽  
Travel Time Prediction ◽  
Resampling Methods ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Travel Time Uncertainty

To a large extent, methods of forecasting travel time have placed emphasis on the quality of the forecasted value—how close is the forecast point estimate of the mean travel time to its respective field value? However, understanding the reliability or uncertainty margin that exists around the forecasted point estimate is also important. Uncertainty about travel time is a fundamental factor as it leads end-users to change their routes and schedules even when the average travel time is low. Statistical resampling methods have been used previously for uncertainty modeling within the travel time prediction environment. This paper applies a recently developed nonparametric resampling method, the gap bootstrap, to the travel time uncertainty estimation problem, especially as it pertains to large (probe) data sets for which common resampling methods may not be practical because of the possible computational burden and complex patterns of inhomogeneity. The gap bootstrap partitions the original data into smaller groups of approximately uniform data sets and recombines individual group uncertainty estimates into a single estimate of uncertainty. Results of the gap bootstrap uncertainty estimates are compared with those of two popular resampling methods—the traditional bootstrap and the block bootstrap. The results suggest that, for the datasets used in this research, the gap bootstrap adequately captures the dependent structure when compared with the traditional and block bootstrap methods and may thus yield more credible estimates of uncertainty than either the block bootstrap method or the traditional bootstrap method.

ON THE JACKKNIFE-AFTER-BOOTSTRAP METHOD FOR DEPENDENT DATA AND ITS CONSISTENCY PROPERTIES

2002 ◽  
Vol 18 (1) ◽  
pp. 79-98 ◽  
Author(s):  
S.N. Lahiri
Keyword(s):  
Large Class ◽  
Standard Error ◽  
Bootstrap Method ◽  
Dependent Data ◽  
Standard Errors ◽  
Error Estimator ◽  
Block Bootstrap ◽  
Royal Statistical Society ◽  
Consistency Properties ◽  
Independent And Identically Distributed

Motivated by Efron (1992, Journal of the Royal Statistical Society, Series B 54, 83–111), this paper proposes a version of the moving block jackknife as a method of estimating standard errors of block-bootstrap estimators under dependence. As in the case of independent and identically distributed (i.i.d.) observations, the proposed method merely regroups the values of a statistic from different bootstrap replicates to produce an estimate of its standard error. Consistency of the resulting jackknife standard error estimator is proved for block-bootstrap estimators of the bias and the variance of a large class of statistics. Consistency of Efron's method is also established in similar problems for i.i.d. data.

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