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Several studies employ multi‐site rs‐fMRI data for major depressive disorder (MDD) identification, with a specific site as the to‐be‐analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter‐site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual‐expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a domain‐generic student model and two domain‐specific teacher/expert models that are jointly trained to perform knowledge distillation through a deep collaborative learning module. A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate multi‐target fMRI harmonization for MDD diagnosis. Comprehensive experiments on 836 subjects with rs‐fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI‐related MDD diagnosis. [ABSTRACT FROM AUTHOR]

Background: Over the past 30 years, MRI has become a ubiquitous tool for accurately visualizing the development of the brain's subcortical structures. However, the quantification of subcortical structures is still in its infancy due to challenges in shape extraction, representation, and modeling. Method: A simple and efficient framework of longitudinal elastic shape analysis (LESA) is developed for subcortical structures. By employing the elastic shape analysis of static surfaces using the square root normal field (SRNF) representations, we computed shape summaries of surfaces and represented the complex subcortical structures using a small number of basis functions. Further, we statistically modeled the sparse longitudinal data to predict the spatial‐temporal shape changes of subcortical structures. We also conducted the temporal registration using the square‐root velocity function (SRVF) framework to quantify the accelerated aging of Alzheimer's disease (AD). Result: We applied LESA to analyze longitudinal neuroimaging data sets and showcased its wide applications in estimating continuous shape trajectories, building life‐span growth patterns, and comparing shape differences among different groups. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, we found that from 60 to 90 years old, the AD group always had the largest left ventricle (LV) surface area and the smallest left hippocampus (LHC) surface area, followed by the MCI and NC groups. The enlargement of LV and the atrophy of LHC had different speeds among different groups. Most of the shape‐changing differences between the AD and NC groups were visible in the anterior and posterior ends of LV. The atrophy of LHC happened mainly at the posterior end with aging. The AD group had the sharpest posterior end, and the NC group deformed the least with aging. Compared with the NC group, the AD group showed an average accelerated enlargement of 2.91 years and accelerated shape change of 2.76 years in LV; an average accelerated atrophy of 2.56 years and accelerated shape change of 2.59 years in LHC. By constructing the life‐span growth trajectories, we observed increases in shape changes' speed in both LV and LHC after 60 years old. Conclusion: AD can significantly speed up the shape change of ventricle and hippocampus compared with normal aging. [ABSTRACT FROM AUTHOR]

Many cancers and neuro‐related diseases display significant phenotypic and genetic heterogeneity across subjects and subpopulations. Characterizing such heterogeneity could transform our understanding of the etiology of these conditions and inspire new approaches to urgently needed prevention, diagnosis, treatment, and prognosis. However, most existing statistical methods face major challenges in delineating such heterogeneity at both the group and individual levels. The aim of this article is to propose a novel statistical disease‐mapping (SDM) framework to address some of these challenges. We develop an efficient estimation method to estimate unknown parameters in SDM and delineate individual and group disease maps. Statistical inference procedures such as hypothesis‐testing problems are also investigated for parameters of interest. Both simulation studies and real data analysis on the ADNI hippocampal surface dataset show that our SDM not only effectively detects diseased regions in each patient but also provides a group disease‐mapping analysis of Alzheimer subgroups. [ABSTRACT FROM AUTHOR]

Medical imaging data have been widely used in modern health care, particularly in the prognosis, screening, diagnosis, and treatment of various diseases. In this study, we consider a latent factor‐on‐image (LoI) regression model that regresses a latent factor on ultrahigh dimensional imaging covariates. The latent factor is characterized by multiple manifest variables through a factor analysis model, while the manifest variables are subject to nonignorable missingness. We propose a two‐stage approach for statistical inference. At the first stage, an efficient functional principal component analysis method is applied to reduce the dimension and extract useful features/eigenimages. At the second stage, a factor analysis mode is proposed to characterize the latent response variable. Moreover, an LoI model is used to detect influential risk factors, and an exponential tiling model applied to accommodate nonignoreable nonresponses. A fully Bayesian method with an adjust spike‐and‐slab absolute shrinkage and selection operator (lasso) procedure is developed for the estimation and selection of influential features/eigenimages. Simulation studies show the proposed method exhibits satisfactory performance. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative data set. [ABSTRACT FROM AUTHOR]

With the advent of high‐throughput sequencing, an efficient computing strategy is required to deal with large genomic data sets. The challenge of estimating a large precision matrix has garnered substantial research attention for its direct application to discriminant analyses and graphical models. Most existing methods either use a lasso‐type penalty that may lead to biased estimators or are computationally intensive, which prevents their applications to very large graphs. We propose using an L0 penalty to estimate an ultra‐large precision matrix (scalnetL0). We apply scalnetL0 to RNA‐seq data from breast cancer patients represented in The Cancer Genome Atlas and find improved accuracy of classifications for survival times. The estimated precision matrix provides information about a large‐scale co‐expression network in breast cancer. Simulation studies demonstrate that scalnetL0 provides more accurate and efficient estimators, yielding shorter CPU time and less Frobenius loss on sparse learning for large‐scale precision matrix estimation. [ABSTRACT FROM AUTHOR]

Background: Proton radiotherapy (PRT) may be a less toxic alternative to photon radiotherapy (XRT) for patients with head and neck squamous cell carcinoma (HNSCC). However, the molecular responses of HNSCC cells to PRT vs XRT are unclear. Methods: Proteomics analyses of protein expression profiles by reverse‐phase protein arrays were done for two human papillomavirus [HPV]‐negative and two HPV+ cell lines. Expression patterns of 175 proteins involved in several signaling pathways were tested. Results: Compared with PRT, XRT tended to induce lower expression of DNA damage repair—and cell cycle arrest‐related proteins and higher expression of cell survival‐ and proliferation‐related proteins. Conclusions: Under these experimental conditions, PRT and XRT induced different protein expression and activation profiles. Further preclinical verification is needed, as are studies of tumor pathway mutations as biomarkers for choice of treatment or as radiosensitization targets to improve the response of HNSCC to PRT or XRT. [ABSTRACT FROM AUTHOR]

Recent evidence suggests the aging process is accelerated by HIV. Degradation of white matter (WM) has been independently associated with HIV and healthy aging. Thus, WM may be vulnerable to joint effects of HIV and aging. Diffusion‐weighted imaging (DWI) was conducted with HIV‐seropositive (n = 72) and HIV‐seronegative (n = 34) adults. DWI data underwent tractography, which was parcellated into 18 WM tracts of interest (TOIs). Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) regression was conducted assessing the joint effect of advanced age and HIV on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along TOI fibers. In addition to main effects of age and HIV on WM microstructure, the interactive effect of age and HIV was significantly related to lower FA and higher MD, AD, and RD across all TOIs. The location of findings was consistent with the clinical presentation of HIV‐associated neurocognitive disorders. While older age is related to poorer WM microstructure, its detrimental effect on WM is stronger among HIV+ relative to HIV− individuals. Loss of WM integrity in the context of advancing age may place HIV+ individuals at increased risk for brain and cognitive compromise. [ABSTRACT FROM AUTHOR]

Clinical applications of 3D image registration and superimposition have contributed to better understanding growth changes and clinical outcomes. The use of 3D dental and craniofacial imaging in dentistry requires validate image analysis methods for improved diagnosis, treatment planning, navigation and assessment of treatment response. Volumetric 3D images, such as cone-beam computed tomography, can now be superimposed by voxels, surfaces or landmarks. Regardless of the image modality or the software tools, the concepts of regions or points of reference affect all quantitative of qualitative assessments. This study reviews current state of the art in 3D image analysis including 3D superimpositions relative to the cranial base and different regional superimpositions, the development of open source and commercial tools for 3D analysis, how this technology has increased clinical research collaborations from centres all around the globe, some insight on how to incorporate artificial intelligence for big data analysis and progress towards personalized orthodontics. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian adaptive group least absolute shrinkage and selection operator method to conduct simultaneous model selection and estimation under semiparametric hidden Markov models. We specify the conditional regression model and the transition probability model in the hidden Markov model into additive nonparametric functions of covariates. A basis expansion is adopted to approximate the nonparametric functions. We introduce multivariate conditional Laplace priors to impose adaptive penalties on regression coefficients and different groups of basis expansions under the Bayesian framework. An efficient Markov chain Monte Carlo algorithm is then proposed to identify the nonexistent, constant, linear, and nonlinear forms of covariate effects in both conditional and transition models. The empirical performance of the proposed methodology is evaluated via simulation studies. We apply the proposed model to analyze a real data set that was collected from the Alzheimer's Disease Neuroimaging Initiative study. The analysis identifies important risk factors on cognitive decline and the transition from cognitive normal to Alzheimer's disease. [ABSTRACT FROM AUTHOR]

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White matter (WM) microstructure, as determined by diffusion tensor imaging (DTI), is increasingly recognized as an important determinant of cognitive function and is also altered in neuropsychiatric disorders. Little is known about genetic and environmental influences on WM microstructure, especially in early childhood, an important period for cognitive development and risk for psychiatric disorders. We studied the heritability of DTI parameters, fractional anisotropy (FA), radial diffusivity (RD) and axial diffusivity (AD) along 34 tracts, including 10 bilateral fiber pathways and the respective subdivision, using quantitative tractography in a longitudinal sample of healthy children at 1 year (N = 215) and 2 years (N = 165) of age. We found that heritabilities for whole brain AD, RD, and FA were 0.48, 0.69, and 0.72 at age 1, and 0.59, 0.77, and 0.76 at age 2 and that mean heritabilities of tract‐averaged AD, RD, and FA for individual bundles were moderate (over 0.4). However, the heritability of DTI change between 1 and 2 years of age was not significant for most tracts. We also demonstrated that point‐wise heritability tended to be significant in the central portions of the tracts and was generally spatially consistent at ages 1 and 2 years. These results, especially when compared to heritability patterns in neonates, indicate that the heritability of WM microstructure is dynamic in early childhood and likely reflect heterogeneous maturation of WM tracts and differential genetic and environmental influences on maturation patterns. [ABSTRACT FROM AUTHOR]

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ℓ0‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology. [ABSTRACT FROM AUTHOR]

We thank all the discussants for sharing their valuable viewpoints on the proposed statistical disease mapping (SDM) framework. In our article, we addressed the issue of imaging heterogeneity at both the global and local scales by efficiently borrowing common information shared among a large number of diseased and normal subjects. Understanding such imaging heterogeneity is critical in the development of urgently needed analytic approaches to the prevention, diagnosis, treatment, and prognosis of many diseases (e.g., Alzheimer's disease, brain cancer, and lung cancer), as well as precision medicine broadly. The discussants emphasized improvements to disease mapping by introducing some alternative modelling strategies and many possible future directions in this research topic. The sections of this rejoinder are organized by discussant to address each of their comments separately. [ABSTRACT FROM AUTHOR]

Genetic and environmental influences on cortical thickness (CT) and surface area (SA) are thought to vary in a complex and dynamic way across the lifespan. It has been established that CT and SA are genetically distinct in older children, adolescents, and adults, and that heritability varies across cortical regions. Very little, however, is known about how genetic and environmental factors influence infant CT and SA. Using structural MRI, we performed the first assessment of genetic and environmental influences on normal variation of SA and CT in 360 twin neonates. We observed strong and significant additive genetic influences on total SA (a2 = 0.78) and small and nonsignificant genetic influences on average CT (a2 = 0.29). Moreover, we found significant genetic overlap (genetic correlation = 0.65) between these global cortical measures. Regionally, there were minimal genetic influences across the cortex for both CT and SA measures and no distinct patterns of genetic regionalization. Overall, outcomes from this study suggest a dynamic relationship between CT and SA during the neonatal period and provide novel insights into how genetic influences shape cortical structure during early development. [ABSTRACT FROM AUTHOR]

Abstract: Coefficient estimation in linear regression models with missing data is routinely carried out in the mean regression framework. However, the mean regression theory breaks down if the error variance is infinite. In addition, correct specification of the likelihood function for existing imputation approach is often challenging in practice, especially for skewed data. In this paper, we develop a novel composite quantile regression and a weighted quantile average estimation procedure for parameter estimation in linear regression models when some responses are missing at random. Instead of imputing the missing response by randomly drawing from its conditional distribution, we propose to impute both missing and observed responses by their estimated conditional quantiles given the observed data and to use the parametrically estimated propensity scores to weigh check functions that define a regression parameter. Both estimation procedures are resistant to heavy‐tailed errors or outliers in the response and can achieve nice robustness and efficiency. Moreover, we propose adaptive penalization methods to simultaneously select significant variables and estimate unknown parameters. Asymptotic properties of the proposed estimators are carefully investigated. An efficient algorithm is developed for fast implementation of the proposed methodologies. We also discuss a model selection criterion, which is based on an ICQ‐type statistic, to select the penalty parameters. The performance of the proposed methods is illustrated via simulated and real data sets. [ABSTRACT FROM AUTHOR]

Connectivity of the prefrontal cortex (PFC) matures through adolescence, coinciding with emergence of adult executive function and top-down inhibitory control over behavior. Alcohol exposure during this critical period of brain maturation may affect development of PFC and frontolimbic connectivity. Adult rats exposed to adolescent intermittent ethanol (AIE; 5 g/kg ethanol, 25 percent v/v in water, intragastrically, 2-day-on, 2-day-off, postnatal day 25-54) or water control underwent resting-state functional MRI to test the hypothesis that AIE induces persistent changes in frontolimbic functional connectivity under baseline and acute alcohol conditions (2 g/kg ethanol or saline, intraperitoneally administered during scanning). Data were acquired on a Bruker 9.4-T MR scanner with rats under dexmedetomidine sedation in combination with isoflurane. Frontolimbic network regions-of-interest for data analysis included PFC [prelimbic (PrL), infralimbic (IL), and orbitofrontal cortex (OFC) portions], nucleus accumbens (NAc), caudate putamen (CPu), dorsal hippocampus, ventral tegmental area, amygdala, and somatosensory forelimb used as a control region. AIE decreased baseline resting-state connectivity between PFC subregions (PrL-IL and IL-OFC) and between PFC-striatal regions (PrL-NAc, IL-CPu, IL-NAc, OFC-CPu, and OFC-NAc). Acute ethanol induced negative blood-oxygen-level-dependent changes within all regions of interest examined, along with significant increases in functional connectivity in control, but not AIE animals. Together, these data support the hypothesis that binge-like adolescent alcohol exposure causes persistent decreases in baseline frontolimbic (particularly frontostriatal) connectivity and alters sensitivity to acute ethanol-induced increases in functional connectivity in adulthood. [ABSTRACT FROM AUTHOR]

Heritability is well documented for psychiatric disorders and cognitive abilities which are, however, complex, involving both genetic and environmental factors. Hence, it remains challenging to discover which and how genetic variations contribute to such complex traits. In this article, they propose to use mediation analysis to bridge this gap, where neuroimaging phenotypes were utilized as intermediate variables. The Philadelphia Neurodevelopmental Cohort was investigated using genome-wide association studies (GWAS) and mediation analyses. Specifically, 951 participants were included with age ranging from 8 to 21 years. Two hundred and four neuroimaging measures were extracted from structural magnetic resonance imaging scans. GWAS were conducted for each measure to evaluate the SNP-based heritability. Furthermore, mediation analyses were employed to understand the mechanisms in which genetic variants have influence on pathological behaviors implicitly through neuroimaging phenotypes, and identified SNPs that would not be detected otherwise. Our analyses found that rs10494561, located in the intron region within NMNAT2, was associated with the severity of the prodromal symptoms of psychosis implicitly, mediated through the volume of the left hemisphere of the superior frontal region ( [ABSTRACT FROM AUTHOR]

The normality assumption of measurement error is a widely used distribution in joint models of longitudinal and survival data, but it may lead to unreasonable or even misleading results when longitudinal data reveal skewness feature. This paper proposes a new joint model for multivariate longitudinal and multivariate survival data by incorporating a nonparametric function into the trajectory function and hazard function and assuming that measurement errors in longitudinal measurement models follow a skew-normal distribution. A Monte Carlo Expectation-Maximization (EM) algorithm together with the penalized-splines technique and the Metropolis-Hastings algorithm within the Gibbs sampler is developed to estimate parameters and nonparametric functions in the considered joint models. Case deletion diagnostic measures are proposed to identify the potential influential observations, and an extended local influence method is presented to assess local influence of minor perturbations. Simulation studies and a real example from a clinical trial are presented to illustrate the proposed methodologies. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion-weighted imaging (DWI) offers a non-invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm-autoMATE (automated Multi-Atlas Tract Extraction); we then extracted multiple DWI-derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method-FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191-1207, 2017. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Parameters in time series and other dynamic models often show complex range restrictions and their distributions may deviate substantially from multivariate normal or other standard parametric distributions. We use the truncated Dirichlet process (DP) as a non-parametric prior for such dynamic parameters in a novel nonlinear Bayesian dynamic factor analysis model. This is equivalent to specifying the prior distribution to be a mixture distribution composed of an unknown number of discrete point masses (or clusters). The stick-breaking prior and the blocked Gibbs sampler are used to enable efficient simulation of posterior samples. Using a series of empirical and simulation examples, we illustrate the flexibility of the proposed approach in approximating distributions of very diverse shapes. [ABSTRACT FROM AUTHOR]

Objective: We aimed to study the neural processing of emotion-denoting words based on a circumplex model of affect, which posits that all emotions can be described as a linear combination of two neurophysiological dimensions, valence and arousal. Based on the circumplex model, we predicted a linear relationship between neural activity and incremental changes in these two affective dimensions. Methods: Using functional magnetic resonance imaging, we assessed in 10 subjects the correlations of BOLD (blood oxygen level dependent) signal with ratings of valence and arousal during the presentation of emotion-denoting words. Results: Valence ratings correlated positively with neural activity in the left insular cortex and inversely with neural activity in the right dorsolateral prefrontal and precuneus cortices. The absolute value of valence ratings (reflecting the positive and negative extremes of valence) correlated positively with neural activity in the left dorsolateral and medial prefrontal cortex (PFC), dorsal anterior cingulate cortex, posterior cingulate cortex, and right dorsal PFC, and inversely with neural activity in the left medial temporal cortex and right amygdala. Arousal ratings and neural activity correlated positively in the left parahippocampus and dorsal anterior cingulate cortex, and inversely in the left dorsolateral PFC and dorsal cerebellum. Conclusion: We found evidence for two neural networks subserving the affective dimensions of valence and arousal. These findings clarify inconsistencies from prior imaging studies of affect by suggesting that two underlying neurophysiological systems, valence and arousal, may subserve the processing of affective stimuli, consistent with the circumplex model of affect. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Previous studies have investigated patterns of volumetric covariance (i.e. intercorrelation) among brain regions. Methodological issues, however, have limited the validity and generalizability of findings from these prior studies. Additionally, patterns of volumetric covariance have often been assumed to reflect the presence of structural networks, but this assumption has never been tested formally. We identified patterns of volumetric covariance, correlated these patterns with behavioral measures, and tested the hypothesis that the observed patterns of covariance reflect the presence of underlying networks. Specifically, we performed factor analysis on regional brain volumes of 99 healthy children and adults, and we correlated factor scores with scores on the Stroop Word-Color Interference Test. We identified four latent volumetric systems in each hemisphere: dorsal cortical, limbic, posterior, and basal ganglia. The positive correlation of the right posterior system with Stroop scores suggested that larger latent volumes are detrimental to inhibitory control. We also applied Structural Equation Modeling (SEM) to our dataset ( n = 107) to test whether a model based on the anatomical pathways within cortico-striatal-thalamic-cortical (CSTC) circuits accounts for the covariances observed in our sample. The degree to which SEM predicted volumetric covariance in the CSTC circuit depended on whether we controlled for age and whole brain volume in the analyses. Removing the effects of age worsened the fit of the model, pointing to a possible developmental component in establishing connections within CSTC circuits. These modeling techniques may prove useful in the future for the study of structural networks in disease populations. Hum Brain Mapp 2008. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

We used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of self-regulatory control across development in healthy individuals performing the Stroop interference task. Proper performance of the task requires the engagement of self-regulatory control to inhibit an automatized response (reading) in favor of another, less automatic response (color naming). Functional MRI scans were acquired from a sample of 70 healthy individuals ranging in age from 7 to 57 years. We measured task-related regional signal changes across the entire cerebrum and conducted correlation analyses to assess the associations of signal activation with age and with behavioral performance. The magnitude of fMRI signal change increased with age in the right inferolateral prefrontal cortex (Brodmann area [BA] 44/45) and right lenticular nucleus. Greater activation of the right inferolateral prefrontal cortex also accompanied better performance. Activity in the right frontostriatal systems increased with age and with better response inhibition, consistent with the known functions of frontostriatal circuits in self-regulatory control. Age-related deactivations in the mesial prefrontal cortex (BA 10), subgenual anterior cingulate cortex (BA 24), and posterior cingulate cortex (BA 31) likely represented the greater engagement of adults in self-monitoring and free associative thought processes during the easier baseline task, consistent with the improved performance on this task in adults compared with children. Although we cannot exclude the possibility that age-related changes in reading ability or in the strategies used to optimize task performance were responsible for our findings, the correlations of brain activation with performance suggest that changes in frontostriatal activity with age underlie the improvement in self-regulatory control that characterizes normal human development. Hum Brain Mapp, 2006. © 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Les auteurs étudient deux estimateurs robustes des composantes de la variance dans un modèle à effets aléatoires. Le calcul du point de rupture de ces estimateurs dans des échantillons de taille finie les amène à proposer un critère de sélection de plans robustes. L'emploi de tels plans permet d'obtenir des estimations efficaces et fiables des composantes de la variance, même en présence d'éventuelles valeurs aberrantes. Des exemples concrets permettent aux auteurs d'illustrer la performance des estimateurs obtenus et de comparer les plans robustes aux plans qui sont optimaux pour l'estimation par l'approche d'analyse de la variance traditionnelle. [ABSTRACT FROM AUTHOR]