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The exact bootstrap method shown on the example of the mean and variance estimation

Kisielinska, Joanna - In: Computational Statistics 28 (2013) 3, pp. 1061-1077

The bootstrap method is based on resampling of the original randomsample drawn from a population with an unknown … parameters or in testing hypotheses. We should stress that in the exact bootstrap method the entire space of resamples is used …. The comparison of the obtained distributions with the limit distributions confirmed the accuracy of the exact bootstrap …

Persistent link: https://www.econbiz.de/10010847838

The uncertainties about the relationships risk–return–volatility in the Spanish stock market

Cao, Ricardo; Heras, Alicia; Saavedra, Angeles - In: Computational Statistics 24 (2009) 1, pp. 113-126

Persistent link: https://www.econbiz.de/10005184311

Permutation tests using least distance estimator in the multivariate regression model

Sohn, Sooncheol; Jung, Byoung; Jhun, Myoungshic - In: Computational Statistics 27 (2012) 2, pp. 191-201

Persistent link: https://www.econbiz.de/10010847642

Nonlinear nonparametric mixed-effects models for unsupervised classification

Azzimonti, Laura; Ieva, Francesca; Paganoni, Anna Maria - In: Computational Statistics 28 (2013) 4, pp. 1549-1570

In this work we propose a novel EM method for the estimation of nonlinear nonparametric mixed-effects models, aimed at unsupervised classification. We perform simulation studies in order to evaluate the algorithm performance and we apply this new procedure to a real dataset. Copyright...

Persistent link: https://www.econbiz.de/10010698287

Testing diffusion processes for non-stationarity

Hamrick, Jeff; Taqqu, Murad - In: Computational Statistics 69 (2009) 3, pp. 509-551

Financial data are often assumed to be generated by diffusions. Using recent results of Fan et al. (J Am Stat Assoc, 102:618–631, 2007; J Financ Econometer, 5:321–357, 2007) and a multiple comparisons procedure created by Benjamini and Hochberg (J R Stat Soc Ser B, 59:289–300, 1995), we...

Persistent link: https://www.econbiz.de/10010759501

The power of bootstrap tests of cointegration rank

Ahlgren, Niklas; Antell, Jan - In: Computational Statistics 28 (2013) 6, pp. 2719-2748

Bootstrap likelihood ratio tests of cointegration rank are commonly used because they tend to have rejection … estimation of the power function of the bootstrap test of cointegration rank. The bootstrap test is found to have a power … function close to that of the level-adjusted asymptotic test. The bootstrap test therefore estimates the level-adjusted power …

Persistent link: https://www.econbiz.de/10010847660

A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches

Liquet, Benoît; Saracco, Jérôme - In: Computational Statistics 27 (2012) 1, pp. 103-125

Persistent link: https://www.econbiz.de/10010847677

Test of misspecification with application to negative binomial distribution

Chua, K.; Ong, S. - In: Computational Statistics 28 (2013) 3, pp. 993-1009

A misspecification test based directly on Bartlett’s First Identity is examined. This test is exemplified by the negative binomial distribution. A Monte Carlo simulation study has been conducted, in the context of testing distributional misspecification, and the performance of the proposed...

Persistent link: https://www.econbiz.de/10010847923

Performance of derivative free search ANN training algorithm with time series and classification problems

Ahmed, Shamsuddin - In: Computational Statistics 28 (2013) 5, pp. 1881-1914

generation model from Fiji Islands and “L-T” letter recognition problems are solved. Bootstrap analysis shows that the algorithm …

Persistent link: https://www.econbiz.de/10010998486

Estimating critical values for testing the i.i.d. in standardized residuals from GARCH models in finite samples

Pérez-Rodríguez, Jorge; Andrada-Félix, Julián - In: Computational Statistics 28 (2013) 2, pp. 701-734

obtains the critical values for the finite sample distribution of the BDS test. We focus on bootstrap simulation to avoid the … experimental results. We consider an extensive grid of models to obtain critical values with the results of the bootstrap … regression models. Specifically, we estimate critical values by using a bootstrap aggregated neural network (BANN) and by …

Persistent link: https://www.econbiz.de/10010998511