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Bayesian approach for mixture models with grouped data

Gau, Shiow-Lan; Tapsoba, Jean Dieu; Lee, Shen-Ming - In: Computational Statistics 29 (2014) 5, pp. 1025-1043

Finite mixture modeling approach is widely used for the analysis of bimodal or multimodal data that are individually observed in many situations. However, in some applications, the analysis becomes substantially challenging as the available data are grouped into categories. In this work, we...

Persistent link: https://www.econbiz.de/10010998492

Copula analysis of mixture models

Vrac, M.; Billard, L.; Diday, E.; Chédin, A. - In: Computational Statistics 27 (2012) 3, pp. 427-457

Contemporary computers collect databases that can be too large for classical methods to handle. The present work takes data whose observations are distribution functions (rather than the single numerical point value of classical data) and presents a computational statistical approach of a new...

Persistent link: https://www.econbiz.de/10010847584

The constrained Fisher scoring method for maximum likelihood computation of a nonparametric mixing distribution

Wang, Yong - In: Computational Statistics 24 (2009) 1, pp. 67-81

Persistent link: https://www.econbiz.de/10005166792

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

Goodness-of-fit tests for a semiparametric model under random double truncation

Moreira, Carla; Uña-Álvarez, Jacobo; Keilegom, Ingrid - In: Computational Statistics 29 (2014) 5, pp. 1365-1379

Doubly truncated data are commonly encountered in areas like medicine, astronomy, economics, among others. A semiparametric estimator of a doubly truncated random variable may be computed based on a parametric specification of the distribution function of the truncation times. This...

Persistent link: https://www.econbiz.de/10010998519

Bootstrap prediction intervals in beta regressions

Espinheira, Patrícia; Ferrari, Silvia; Cribari-Neto, … - In: Computational Statistics 29 (2014) 5, pp. 1263-1277

> </EquationSource> </InlineEquation> (bias-corrected and accelerated) bootstrap prediction intervals. We present Monte Carlo evidence on …

Persistent link: https://www.econbiz.de/10010998523

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

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