Ducharme G. and Lafaye de Micheaux P. Goodness-of-fit tests of normality for the innovations in ARMA models. Journal of Time Series Analysis, Vol. 25, No. 3, p.373-395, (2004).
Bilodeau M. and Lafaye de Micheaux P. A multivariate empirical characteristic function test of independence with normal marginals. Journal of Multivariate Analysis, Volume 95, Issue 2, August, p. 345-369, (2005).
Beran R., Bilodeau M. and Lafaye de Micheaux P. Nonparametric tests of independence between random vectors. Journal of Multivariate Analysis, Volume 98, Number 9, p. 1805-1824, (2007).
Dubois M., Lafaye de Micheaux P., Noël M.P. and Valdois S., Pre-orthographical constraints on visual word recognition: Evidence from a case study of developmental surface dyslexia. Cognitive Neuropsychology, 24(6), pages 1-39, (2007).
Cenier T., Amat C., Litaudon P., Garcia S., Lafaye de Micheaux P., Liquet B., Roux S. and Buonviso N., Odor vapor pressure and quality modulate local field potential oscillatory patterns in the olfactory bulb of the anesthetized rat. European Journal of Neuroscience, 28(6), march, (2008).
Lafaye de Micheaux P. and Liquet B., Understanding Convergence Concepts : a Graphical Simulation Based Approach. American Statistician, 7 pages, 63(2), p. 173-178, (2008).
Bilodeau M., Lafaye de Micheaux P., A-dependence statistics for mutual and serial independence of categorical variables. Journal of Statistical Planning and Inference, 139(7), p. 2407-2419, (2009).
Lafaye de Micheaux P., Liquet, B., ConvergenceConcepts: an R to investigate various modes of convergence. The RJournal, 1(2), p. 18-25, december (2009).
Coeurjolly J.F., Drouilhet R., Lafaye de Micheaux P. and Robineau J.F., asympTest: A simple R for performing classical parametric statistical tests and confidence intervals in large samples. The RJournal, 1(2), p. 26-30, december (2009)..
Duchesne, P., Lafaye de Micheaux P., Computing the distribution of quadratic forms: Further comparisons between the Liu-Tan-Zhang approximation and exact methods. Computational Statistics and Data Analysis, 54(4), p. 858-862, april (2010).
Tabelow K., Clayden J.D., Lafaye de Micheaux P., Polzehl J., Schmid V.J., Whitcher B., Image Analysis and Statistical Inference in Neuroimaging with R. NeuroImage, 55(4), p. 1686-1693, (2011).
Bordier C., Dojat, M. and Lafaye de Micheaux P., Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R package. Journal of Statistical Software, Special Volume: Magnetic Resonance Imaging in R, 44(9), (2011).
Lafaye de Micheaux P. and Léger C., A law of the single logarithm for weighted sums of arrays applied to bootstrap model selection in regression. Statistics and Probability Letters, 82(5) p. 965–971, (2012).
Lafaye de Micheaux P. and Lemaire V., Sample size determination and statistical hypothesis testing for core centration in press coated tablets. Open Journal of Statistics, 2(3) p. 269–273, (2012).
Desgagné A., Lafaye de Micheaux P. and Leblanc A., Test of normality against generalized exponential power alternatives. Communications in Statistics, Theory and Methods, 42(1) p. 164–190, (2013).
Duchesne P. and Lafaye de Micheaux P., Distributions for residual autocovariances in parsimonious periodic vector autoregressive models with applications. Journal of Time Series Analysis, 34(4) p. 496–507, (2013).
Nicoli F., Lafaye de Micheaux P. and Girard N., Perfusion-Weighted Imaging-Derived Collateral Flow Index is a Predictor of MCA M1 Recanalization After IV Thrombolysis. American Journal of Neuroradiology, 34(1) p. 107–114, (2013).
Marteau P., Guyonnet D., Lafaye de Micheaux P. and Gelu S., A randomized, double-blind, controlled study and pooled analysis of two identical trials of fermented milk containing probiotic Bifidobacterium lactis CNCM I-2494 in healthy women reporting minor digestive symptoms. Neurogastroenterology and Motility, 25(4) p. 331–e252, (2013).
Lafaye de Micheaux P., Liquet B., Marque S. and Riou J., Power and sample size determination in clinical trials with multiple primary continuous correlated endpoints. Journal of Biopharmaceutical Statistics, 24(2), p. 378-397, (2014).
PUBLICATIONS details.
[1]Goodness-of-fit tests of normality for the innovations in ARMA models, 2004.
Abstract: In this paper, we propose a goodness-of-fit test of normality for the innovations of an ARMA(p,q) model with known mean or trend. The test is based on the data-driven smooth test approach and is simple to perform. An extensive simulation study is conducted to see if, for moderate sample sizes, the test holds its level throughout the parameter space. The power of the procedure is also explored by simulation. It is found that our test is generally more powerful than existing tests while holding its level throughout most of the parameter space and thus, can be recommended. This meshes with theoretical results showing the superiority of the data-driven smooth test approach in related contexts.
Abstract: This paper proposes a semi-parametric test of independence (or serial independence) between marginal vectors each of which is normally distributed but without assuming the joint normality of these marginal vectors. The test statistic is a Cramér-von Mises functional of a process defined from the empirical characteristic function. This process is defined similarly as the process of Ghoudi et al. (2001) built from the empirical distribution function and used to test for independence between univariate marginal variables. The test statistic can be represented as a V statistic. It is consistent to detect any form of dependence. The weak convergence of the process is derived. The asymptotic distribution of the Cramér-von Mises functionals is approximated by the Cornish-Fisher expansion using a recursive formula for cumulants and by the numerical evaluations of the eigenvalues in the inversion formula. The test statistic is finally compared with Wilks statistic for testing the parametric hypothesis of independence in the one-way MANOVA model with random effects.
[3]Nonparametric tests of independence between random vectors, 2007.
Abstract: A non parametric test of the mutual independence between many numerical random vectors is proposed. This test is based on a characterization of mutual independence defined from probabilities of half-spaces in a combinatorial formula of Moebius. As such, it is a natural generalization of tests of independence between univariate random variables using the empirical distribution function. If the number of vectors is p and there are n observations, the test is defined from a collection of processes Rn,A , where A is a subset of {1, . . . , p} of cardinality |A| > 1, which are asymptotically independent and Gaussian. Without the assumption that each vector is one-dimensional with a continuous cumulative distribution function, any test of independence can not be distribution free. The critical values of the proposed test are thus computed with the bootstrap which is shown to be consistent. Another similar test, with the same asymptotic properties, for the serial independence of a multivariate stationary sequence is also proposed. The proposed test works when some or all of the marginal distributions are singular with respect to Lebesgue measure. Moreover, in singular cases described in Section 4, the test inherits useful invariance properties from the general affine invariance property.
[4]Pre-orthographical constraints on visual word recognition: Evidence from a case study of developmental surface dyslexia, 2007.
Abstract: We investigated the visual word recognition ability of M.T., a young boy with surface dyslexia, by means of a paradigm that measures performance as a function of the eye fixation position within the word, known as the "viewing-position effect" paradigm. In well-achieving readers, the viewing- position effect is mainly determined by factors affecting letter visibility and by lexical constraints on word recognition. We further quantified M.T.'s sensory limitations on letter visibility by computing visual-span profiles--that is, the number of letters recognizable at a glance. Finally, in an ideal- observer's perspective, M.T.'s performance was compared with a parameter-free model combining M.T.'s letter visibility data with a simple lexical matching rule. The results showed that M.T. did not use the whole visual information available on letter identities to recognize words and that pre- orthographical factors constrained his word recognition performance. The results can be best accounted for by a reduction of the number of letters processed in parallel.
[5]Odor vapor pressure and quality modulate local field potential oscillatory patterns in the olfactory bulb of the anesthetized rat, 2008.
Abstract: A central question in chemical senses is the way that odorant molecules are represented in the brain. To date, many studies, when taken together, suggest that structural features of the molecules are represented through a spatio-temporal pattern of activation in the olfactory bulb (OB), in both glomerular and mitral cell layers. Mitral / tufted cells interact with a large population of inhibitory interneurons resulting in a temporal patterning of bulbar local field potential (LFP) activity. We investigated the possibility that molecular features could determine the temporal pattern of LFP oscillatory activity in the OB. For this purpose, we recorded the LFPs in the OB of urethane-anesthetized, freely breathing rats in response to series of aliphatic odorants varying subtly in carbon-chain length or functional group. In concordance with our previous reports, we found that odors evoked oscillatory activity in the LFP signal in both the beta and gamma frequency bands. Analysis of LFP oscillations revealed that, although molecular features have almost no influence on the intrinsic characteristics of LFP oscillations, they influence the temporal patterning of bulbar oscillations. Alcohol family odors rarely evoke gamma oscillations, whereas ester family odors rather induce oscillatory patterns showing beta / gamma alternation. Moreover, for molecules with the same functional group, the probability of gamma occurrence is correlated to the vapor pressure of the odor. The significance of the relation between odorant features and oscillatory regimes along with their functional relevance are discussed.
[6]Understanding Convergence Concepts : a Graphical Simulation Based Approach, 2008.
Abstract: This paper describes the difficult concepts of convergence in probability, almost sure convergence, convergence in law and in r-th mean using a visual-minded and a graphical simulation-based approach. For this purpose, each probability of events is approximated by a frequency. An R package is available on CRAN which reproduces all the experiments done in this paper.
[7] A-dependence statistics for mutual and serial independence of categorical variables, 2009.
Abstract: The Mobius transformation of probability cells in a multi-way contingency table is used to partition the Pearson chi-square test of mutual independence into A-de\-pen\-dence statistics. A similar partition is proposed for a universal and consistent test of serial independence in a stationary sequence of a categorical variable. The partition proposed can be adapted whether using estimated or theoretical marginal probabilities. With the aim of detecting a dependence of high order in a long sequence, $A$-de\-pen\-dence terms of the partition measuring increasing lagged dependences can be combined in a Box-Pierce type test of serial independence. A real data analysis of a nucleotides sequence using the Box-Pierce type test is provided.
[8] ConvergenceConcepts: an R to investigate various modes of convergence, 2009.
Abstract: ConvergenceConcepts is an R package, built upon the tkrplot, tcltk and lattice packages, designed to investigate the convergence of simulated sequences of random variables. Four classical modes of convergence may be studied, namely: almost sure convergence (a.s.), convergence in probability (P), convergence in law (L) and convergence in r-th mean (r). This investigation is performed through accurate graphical representations. This package may be used as a pedagogical tool. It may give students a better understanding of these notions and help them to visualize these difficult theoretical concepts. Moreover, some scholars could gain some insight into the behaviour of some random sequences they are interested in.
[9] asympTest: A simple R for performing classical parametric statistical tests and confidence intervals in large samples, 2009.
Abstract: asympTest is an R package implementing large sample tests and confidence intervals. One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software.
[10] Computing the distribution of quadratic forms: Further comparisons between the Liu-Tan-Zhang approximation and exact methods, 2009.
Abstract: Liu, Tang and Zhang [Liu, H. Tang, Y., Zhang H.H. 2009. A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables. Computational Statistics & Data Analysis 53, 853856] proposed a chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables. To approximate the distribution of interest, they used a non-central chi-square distribution, where the degrees of freedom and the non-centrality parameter were calculated using the first four cumulants of the quadratic form. Numerical examples were encouraging, suggesting that the approximation was particularly accurate in the upper tail of the distribution. We present here additional empirical evidence, comparing LiuTangZhang's four-moment non-central chi-square approximation with exact methods. While the moment-based method is interesting because of its simplicity, we demonstrate that it should be used with care in practical work, since numerical examples suggest that significant differences may occur between that method and exact methods, even in the upper tail of the distribution.
[11] Image Analysis and Statistical Inference in Neuroimaging with R, 2009.
Abstract: R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.
[12] Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R package, 2011.
Abstract : For statistical analysis of functional magnetic resonance imaging (fMRI) data sets, we propose a data-driven approach based on independent component analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.
[13] A law of the single logarithm for weighted sums of arrays applied to bootstrap model selection in regression, 2012.
Abstract : We generalize a law of the single logarithm obtained by Qi (1994) and Li et al. (1995) to the case of weighted sums of triangular arrays of random variables. We apply this result to bootstrapping the all-subsets model selection problem in regression, where we show that the popular Bayesian Information Criterion of Schwarz (1978) is no longer asymptotically consistent.
[14] Sample size determination and statistical hypothesis testing for core centration in press coated tablets, 2012.
Abstract : A novel statistical approach to evaluate the manufacturing quality of press coated tablets in terms of the centering of their core is presented. We also provide a formula to determine the necessary sample size. This approach is applied to real data.
[15] Test of normality against generalized exponential power alternatives, 2013.
Abstract : The family of symmetric generalized exponential power (GEP) densities offers a wide range of tail behaviours, which may be exponential, polynomial and/or logarithmic. In this paper, a test of normality based on Rao's score statistic and this family of GEP alternatives is proposed. This test is tailored to detect departures from normality in the tails of the distribution. The main interest of this approach is that it provides a test with a large family of symmetric alternatives having non-normal tails. In addition, the test's statistic consists of a combination of three quantities that can be interpreted as new measures of tail thickness. In a Monte-Carlo simulation study, the proposed test is shown to perform well in terms of power when compared to its competitors.
[16] Distributions for residual autocovariances in parsimonious periodic vector autoregressive models with applications, 2013.
Abstract : The asymptotic distribution of the residual autocovariance matrices in the class of periodic vector autoregressive time series models with structured parameterization is derived. Diagnostic checking with
portmanteau test statistics represents a useful application of the result. Under the assumption that the periodic
white noise process of the periodic vector autoregressive time series model is composed of independent
random variables, we demonstrate that the finite sample distributions of the Hosking-Li-McLeod portmanteau
test statistics can be approximated by those of weighted sums of independent chi-square random variables.
The quantiles of the asymptotic distribution can be computed using the Imhof algorithm or other exact
methods. Thus, using the (single) chi-square distribution for these test statistics appears inadequate in general,
although it is often recommended in practice for diagnostic methods of that kind. A simulation study provides
empirical evidence.
[17] Perfusion-Weighted Imaging-Derived Collateral Flow Index is a Predictor of MCA M1 Recanalization After IV Thrombolysis, 2013.
Abstract : Recent studies highlight the role of CC in preserving ischemic penumbra. Some authors suggested the quality of CC could also impact recanalization. The purpose of this study is to test this hypothesis in patients who were treated with IV thrombolysis for MCA-M1 occlusion.
[18] A randomized, double-blind, controlled study and pooled analysis of two identical trials of fermented milk containing probiotic Bifidobacterium lactis CNCM I-2494 in healthy women reporting minor digestive symptoms, 2013.
Abstract : The probiotic fermented milk (PFM)
containing Bifidobacterium lactis CNCM I-2494 improved gastrointestinal (GI) well-being and digestive symptoms in a previous trial involving women reporting minor digestive symptoms. Our objective is to confirm these findings in a second study and in a
pooled analysis of both studies.
[19] Power and sample size determination in clinical trials with multiple primary continuous correlated endpoints, 2014.
Abstract : The use of two or more primary correlated endpoints is becoming increasingly common. A mandatory approach when analyzing data from such clinical trials is to control the Familywise Error Rate (FWER). In this context,
we provide formulas for computation of sample size, and for data analysis. Two approaches are discussed: an individual method based on a union-intersection procedure and a global procedure based on a multivariate model which can take
into account adjustment variables. These methods are illustrated with simulation studies and applications. An R package known as SampleSize is also available.
