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Robust Inferences (robust + inference)

Distribution by Scientific Domains
Mathematics and Statistics25%
Life Sciences25%
Business, Economics, Finance and Accounting25%

Selected Abstracts

Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples

BIOMETRICS, Issue 1 2006
Raphael Gottardo
Summary We consider the problem of identifying differentially expressed genes under different conditions using gene expression microarrays. Because of the many steps involved in the experimental process, from hybridization to image analysis, cDNA microarray data often contain outliers. For example, an outlying data value could occur because of scratches or dust on the surface, imperfections in the glass, or imperfections in the array production. We develop a robust Bayesian hierarchical model for testing for differential expression. Errors are modeled explicitly using a t -distribution, which accounts for outliers. The model includes an exchangeable prior for the variances, which allows different variances for the genes but still shrinks extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and it can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. The method is illustrated using two publicly available gene expression data sets. We compare our method to six other baseline and commonly used techniques, namely the t -test, the Bonferroni-adjusted t -test, significance analysis of microarrays (SAM), Efron's empirical Bayes, and EBarrays in both its lognormal,normal and gamma,gamma forms. In an experiment with HIV data, our method performed better than these alternatives, on the basis of between-replicate agreement and disagreement. [source]

Robust inference in generalized linear models for longitudinal data

THE CANADIAN JOURNAL OF STATISTICS, Issue 2 2006
Sanjoy K. Sinha
Abstract The author develops a robust quasi-likelihood method, which appears to be useful for down-weighting any influential data points when estimating the model parameters. He illustrates the computational issues of the method in an example. He uses simulations to study the behaviour of the robust estimates when data are contaminated with outliers, and he compares these estimates to those obtained by the ordinary quasi-likelihood method. Inférence robuste pour des modèles de données longitudinales linéaires généralisés L'auteur développe une méthode de quasi-vraisemblance robuste qui semble utile pour réduire l'impact des points influents sur l'estimation des paramètres d'un modèle. Il illustre les questions de calcul liées à la méthode à l'aide d'un exemple. Il a recours à des simulations pour étudier le comportement des estimations robustes lorsque les données sont contaminées par des valeurs aberrantes et il compare ces estimations à celles obtenues par la méthode de quasi-vraisemblance ordinaire. [source]

Was the Australian Meat and Live-stock Corporation's advertising efficient?

AUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 1 2000
Garth J. Holoway
A theory of the allocation of producer levies earmarked for downstream promotion is developed and applied to quarterly series (1970:2,1988:4) on red-meats advertising by the Australian Meat and Live-stock Corporation. Robust inferences about program efficiency are contained in the coefficients of changes in promotion effort regressed against movements in farm price and quantity. Empirical evidence of program efficiency is inconclusive. While the deeper issue of efficient disbursement of funds remains an open question, there is evidence, at least, of efficient taxation. [source]

Bootstrapping Autoregression under Non-stationary Volatility

THE ECONOMETRICS JOURNAL, Issue 1 2008
Ke-Li Xu
Summary This paper studies robust inference in autoregression around a polynomial trend with stable autoregressive roots under non-stationary volatility. The formulation of the volatility process is quite general including many existing deterministic and stochastic non-stationary volatility specifications. The aim of the paper is two-fold. First, it develops a limit theory for least squares estimators and shows how non-stationary volatility affects the consistency, convergence rates and asymptotic distributions of the slope and trend coefficients estimators in different ways. This complements the results recently obtained by Chung and Park (2007, Journal of Econometrics 137, 230,59. Second, it studies the recursive wild bootstrap procedure of Gonçalves and Kilian (2004, Journal of Econometrics 123, 89,120) in the presence of non-stationary volatility, and shows its validity when the estimates are asymptotically mixed Gaussian. Simulations are performed to compare favourably the recursive wild bootstrap with other inference procedures under non-stationary volatility. [source]

Corporate discourse and environmental performance in Argentina

BUSINESS STRATEGY AND THE ENVIRONMENT, Issue 3 2008
Diego A. Vazquez
Abstract There is substantial research and policy interest in the relationship between firms and the natural environment, including how this relationship is influenced by regulators, international pressures, rival firms and stakeholder demands. With some exceptions, the ,softer' dimensions of environmental aspect management , how attitudes, beliefs and perceptions and the human factors drive corporate behaviour , have been understudied. The work that exists tends to be informal, and allows little scope for the statistical validation that is required for robust inference. This paper examines whether corporate values towards the environment affect a firm's environmental performance. It uses survey methods as well as content and discourse analyses of interview text and corporate reports and web sites to explore the links between managerial ,mindsets' and the environmental performance of the firms of which they are a part. Though the application is Argentina, the lessons learned can be generalized to other developing and industrialized countries. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment. [source]

Reconstructing ancestral ecologies: challenges and possible solutions

DIVERSITY AND DISTRIBUTIONS, Issue 1 2006
Christopher R. Hardy
ABSTRACT There are several ways to extract information about the evolutionary ecology of clades from their phylogenies. Of these, character state optimization and ,ancestor reconstruction' are perhaps the most widely used despite their being fraught with assumptions and potential pitfalls. Requirements for robust inferences of ancestral traits in general (i.e. those applicable to all types of characters) include accurate and robust phylogenetic hypotheses, complete species-level sampling and the appropriate choice of optimality criterion. Ecological characters, however, also require careful consideration of methods for accounting for intraspecific variability. Such methods include ,Presence Coding' and ,Polymorphism Coding' for discrete ecological characters, and ,Range Coding' and ,MaxMin Coding' for continuously variable characters. Ultimately, however, historical inferences such as these are, as with phylogenetic inference itself, associated with a degree of uncertainty. Statistically based uncertainty estimates are available within the context of model-based inference (e.g. maximum likelihood and Bayesian); however, these measures are only as reliable as the chosen model is appropriate. Although generally thought to preclude the possibility of measuring relative uncertainty or support for alternative possible reconstructions, certain useful non-statistical support measures (i.e. ,Sharkey support' and ,Parsimony support') are applicable to parsimony reconstructions. [source]

The hyomandibulae of rhizodontids (Sarcopterygii, stem-tetrapoda)

JOURNAL OF MORPHOLOGY, Issue 6 2008
Martin D. Brazeau
Abstract Despite its important role in the study of the evolution of tetrapods, the hyomandibular bone (the homologue of the stapes in crown-group tetrapods) is known for only a few of the fish-like members of the tetrapod stem-group. The best-known example, that of the tristichopterid Eusthenopteron, has been used as an exemplar of fish-like stem-tetrapod hyomandibula morphology, but in truth the conditions at the base of the tetrapod radiation remain obscure. We report, here, four hyomandibulae, from three separate localities, which are referable to the Rhizodontida, the most basal clade of stem-tetrapods. These specimens share a number of characteristics, and are appreciably different from the small number of hyomandibulae reported for other fish-like stem-tetrapods. While it is unclear if these characteristics represent synapomorphies or symplesiomorphies, they highlight the morphological diversity of hyomandibulae within the early evolution of the tetrapod total-group. Well-preserved muscle scarring on some of these hyomandibulae permit more robust inferences of hyoid arch musculature in stem-tetrapods. J. Morphol., 2008. © 2008 Wiley-Liss, Inc. [source]

Twenty-five pitfalls in the analysis of diffusion MRI data,

NMR IN BIOMEDICINE, Issue 7 2010
Derek K. Jones
Abstract Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI. Copyright © 2010 John Wiley & Sons, Ltd. [source]