. Example and K. Good, in the upper part of the figure (raw data, histogram and estimated GM density on the left, estimated empirical and GM cdf on the right), a GM with 4 clusters is fitted to some data, and in the lower part a GM with 3 clusters is fitted to the same data, which leads to bad fitting and a high value of, p.26

Q. Histogram, 1000 p-values generated under a GM model, for n = 30 and under 3 possible levels of overlapping of clusters: a uniform distribution is expected for a good calibration of the test (for the QQ-plot, uniformity means being very close to the diagonal line), p.32

I. References-[-bab11 and ]. G. Babu, Resampling methods for model fitting and model selection, Journal of Biopharmaceutical Statistics, vol.21, pp.1177-1186, 2011.

J. Peter, R. A. Brockwell, and . Davis, Introduction to Time Series and Forecasting, pp.978-0387953519, 2002.

[. Babu and C. R. Rao, Goodness-of-fit tests when parameters are estimated . Sankhy¯ a : The Indian Journal of, Statistics, vol.66, issue.1, pp.63-74, 2004.

J. [. Cassam-chenaï and . Liévin, Alternative perturbation method for the molecular vibration???rotation problem, International Journal of Quantum Chemistry, vol.93, issue.3, pp.245-264, 2003.
DOI : 10.1002/qua.10556

J. [. Cassam-chenaï and . Liévin, The VMFCI method: A flexible tool for solving the molecular vibration problem, Journal of Computational Chemistry, vol.99, issue.5, pp.627-640, 2006.
DOI : 10.1002/jcc.20374

R. Michael, . Chernick, D. R. Chauveau, M. Hunter, and . Levine, Bootstrap Methods: A Guide for Practitioners and Researchers Wiley series in Probability and Statistics Semi-parametric estimation for conditional independence multivariate finite mixture models, Statistics Surveys, vol.9, pp.1-31, 2007.

D. [. Davison and . Hinkley, Bootstrap Methods and their Application, Cambridge Series in Statistical and Probabilistic Mathematics, 1997.
DOI : 10.1017/CBO9780511802843

N. [. Dempster, D. B. Laird, and . Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society, Series B, vol.39, issue.1, pp.1-38, 1977.

R. B. Agostino and M. A. Stephens, Goodness-of-fit techniques, 1986.

[. Fraley, A. Raftery, B. Murphy, and L. Scrucca, mclust version 4 for r : Normal mixture modeling for model-based clustering, classification, and density estimation, 2012.

A. Fujita, D. Y. Takahashi, and A. G. Patriota, A non-parametric method to estimate the number of clusters, Computational Statistics & Data Analysis, vol.73, pp.27-39
DOI : 10.1016/j.csda.2013.11.012

A. Georges, D. Buytaert, and L. Eeckhout, Statistically rigorous java performance evaluation, Proceedings of the Twenty-Second ACM SIGPLAN Conference on OOPSLA, pp.57-76, 2007.
DOI : 10.1145/1297105.1297033

I. Jain, The Art of Computer Systems Performance Analysis : Techniques for Experimental Design, Measurement, Simulation, and Modelling, pp.978-978, 1991.

W. Jalby, C. Lemuet, and S. Touati, An efficient memory operations optimization technique for vector loops on Itanium 2 processors, Concurrency and Computation: Practice and Experience, vol.17, issue.11, pp.1485-1508, 2006.
DOI : 10.1002/cpe.1017

I. Kojadinovic and J. Yan, Goodness-of-fit testing based on a weighted bootstrap: A fast large-sample alternative to the parametric bootstrap, Canadian Journal of Statistics, vol.43, issue.403, pp.480-500, 2012.
DOI : 10.1002/cjs.11135
URL : https://hal.archives-ouvertes.fr/hal-00865050

J. David and . Lilja, Measuring Computer Performance: A Practitioner's Guide, pp.978-0521641050, 2000.

[. Lemuet, W. Jalby, and S. Touati, Improving load/store queues usage in scientific computing, International Conference on Parallel Processing, 2004. ICPP 2004., 2004.
DOI : 10.1109/ICPP.2004.1327902
URL : https://hal.archives-ouvertes.fr/inria-00637256

[. Leather, O. Michael, B. Boyle, and . Worton, Raced Profiles: Efficient Selection of Competing Compiler Optimizations, Conference on Languages, Compilers , and Tools for Embedded Systems (LCTES '09), 2009.

[. Mytkowicz, A. Diwan, P. F. Sweeney, and M. Hauswirth, Producing wrong data without doing anything obviously wrong, ASPLOS, 2009. [MP00] Geoffrey MacLachlan and David Peel. Finite Mixture Models. Wiley series in Probability and Statistics, 2000.

[. Mazouz, A. Touati, and D. Barthou, Study of Variations of Native Program Execution Times on Multi-Core Architectures, 2010 International Conference on Complex, Intelligent and Software Intensive Systems, 2010.
DOI : 10.1109/CISIS.2010.96
URL : https://hal.archives-ouvertes.fr/hal-00643731

[. , V. Rivoirard, F. Grammont, and C. Tuleau-malot, Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis, The Journal of Mathematical Neuroscience, vol.4, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00789127

W. [. Stute, M. P. Manteiga, and . Quindimil, Bootstrap based goodness-of-fit-tests, Metrika, vol.66, issue.1, pp.242-256, 1993.
DOI : 10.1007/BF02613687

]. O. Tha10 and . Thas, Comparing Distributions, 2010.

A. [. Titterington, U. E. Smith, and . Makov, Statistical Analysis of Finite Mixture Distributions, 1985.

[. Touati, J. Worms, and S. Briais, The Speedup-Test University of Versailles Saint-Quentin en Yvelines Research report number HAL-inria-00443839, 2010.

[. Touati, J. Worms, and S. Briais, The Speedup-Test: A Statistical Methodology for Program Speedup Analysis and Computation. Concurrency and Computation: Practice and Experience, pp.1410-1426, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00764454

[. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.
DOI : 10.1111/1467-9868.00293

]. A. Van and . Vaart, Asymptotic statistics, 2000.