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PubMed2005

Power and robustness of linkage tests for quantitative traits in general pedigrees.

There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this report, we use computer simulations to investigate the power and robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method.

Algorithms↗
PubMed2002

Mapping myocardial perfusion with an intravascular MR contrast agent: robustness of deconvolution methods at various blood flows.

Evaluation of quantitative parameters such as regional myocardial blood flow (rMBF), blood volume (rMBV), and mean transit time (rMTT) by MRI is gaining acceptance for clinical applications, but still lacks robust postprocessing methods for map generation. Moreover, robustness should be preserved over the full range of myocardial flows and volumes. Using experimental data from an isolated pig heart preparation, synthetic MR kinetics were generated and four deconvolution approaches were evaluated. These methods were then applied to the first-pass T(1) images of the isolated pig heart using an intravascular contrast agent and rMBF, rMBV and rMTT maps were generated. In both synthetic and experimental data, the fit between calculated and original data reached equally good results with the four techniques. rMBV was the only parameter estimated correctly in numerical experiments. Moreover, using the algebraic method ARMA, abnormal regions were well delineated on rMBV maps. At high flows, rMBF was underestimated at the experimental noise level. Finally, rMTT maps appeared noisy and highly unreliable, especially at high flows. In conclusion, over the myocardial flow range, i.e., 0-400 ml/min/100g, rMBF identification was biased in presence of noise, whereas rMBV was correctly identified. Thus, rMBV mapping could be a fast and robust way to detect abnormal myocardial regions.

Animals↗
PubMed2007

Statistical evidence for GLM regression parameters: a robust likelihood approach.

When a likelihood ratio is used to measure the strength of evidence for one hypothesis over another, its reliability (i.e. how often it produces misleading evidence) depends on the specification of the working model. When the working model happens to be the 'true' or 'correct' model, the probability of observing strong misleading evidence is low and controllable. But this is not necessarily the case when the working model is misspecified. Royall and Tsou (J. R. Stat. Soc., Ser. B 2003; 65:391-404) show how to adjust working models to make them robust to misspecification. Likelihood ratios derived from their 'robust adjusted likelihood' are just as reliable (asymptotically) as if the working model were correctly specified in the first place. In this paper, we apply and extend these ideas to the generalized linear model (GLM) regression setting. We provide several illustrations (both from simulated data and real data concerning rates of parasitic infection in Philippine adolescents), show how the required adjustment factor can be obtained from standard statistical software, and draw some connections between this approach and the 'sandwich estimator' for robust standard errors of regression parameters. This substantially broadens the availability and the viability of likelihood methods for measuring statistical evidence in regression settings.

Adolescent↗
PubMed1993

An algorithm for robust non-linear analysis of radioimmunoassays and other bioassays.

The four-parameter logistic function is an appropriate model for many types of bioassays that have continuous response variables, such as radioimmunoassays. By modelling the variance of replicates in an assay, one can modify the usual parameter estimation techniques (for example, Gauss-Newton or Marquardt-Levenberg) to produce parameter estimates for the standard curve that are robust against outlying observations. This article describes the computation of robust (M-) estimates for the parameters of the four-parameter logistic function. It describes techniques for modelling the variance structure of the replicates, modifications to the usual iterative algorithms for parameter estimation in non-linear models, and a formula for inverse confidence intervals. To demonstrate the algorithm, the article presents examples where the robustly estimated four-parameter logistic model is compared with the logit-log and four-parameter logistic models with least-squares estimates.

Algorithms↗
PubMed1995

Robust Bayesian methods for monitoring clinical trials.

Bayesian methods for the analysis of clinical trials data have received increasing attention recently as they offer an approach for dealing with difficult problems that arise in practice. A major criticism of the Bayesian approach, however, has focused on the need to specify a single, often subjective, prior distribution for the parameters of interest. In an attempt to address this criticism, we describe methods for assessing the robustness of the posterior distribution to the specification of the prior. The robust Bayesian approach to data analysis replaces the prior distribution with a class of prior distributions and investigates how the inferences might change as the prior varies over this class. The purpose of this paper is to illustrate the application of robust Bayesian methods to the analysis of clinical trials data. Using two examples of clinical trials taken from the literature, we illustrate how to use these methods to help a data monitoring committee decide whether or not to stop a trial early.

Bayes Theorem↗
PubMed2002

Why are proteins so robust to site mutations?

There have been repeated observations that proteins are surprisingly robust to site mutations, enduring significant numbers of substitutions with little change in structure, stability, or function. These results are almost paradoxical in light of what is known about random heteropolymers and the sensitivity of their properties to seemingly trivial mutations. To address this discrepancy, the preservation of biological protein properties in the presence of mutation has been interpreted as indicating the independence of selective pressure on such properties. Such results also lead to the prediction that de novo protein design should be relatively easy, in contrast to what is observed. Here, we use a computational model with lattice proteins to demonstrate how this robustness can result from population dynamics during the evolutionary process. As a result, sequence plasticity may be a characteristic of evolutionarily derived proteins and not necessarily a property of designed proteins. This suggests that this robustness must be re-interpreted in evolutionary terms, and has consequences for our understanding of both in vivo and in vitro protein evolution.

Computer Simulation↗
PubMed2005

Robust pulmonary nodule segmentation in CT: improving performance for juxtapleural cases.

Two novel methods are proposed for robust segmentation of pulmonary nodules in CT images. The proposed solutions locate and segment a nodule in a semi-automatic fashion with a marker indicating the target. The solutions are motivated for handling the difficulty to segment juxtapleural, or wall-attached, nodules by using only local information without a global lung segmentation. They are realized as extensions of the recently proposed robust Gaussian fitting approach. Algorithms based on i) 3D morphological opening with anisotropic structuring element and ii) extended mean shift with a Gaussian repelling prior are presented. They are empirically compared against the robust Gaussian fitting solution by using a large clinical high-resolution CT dataset. The results show 8% increase, resulting in 95% correct segmentation rate for the dataset.

Algorithms↗
PubMed2004

Periodical in-situ re-calibration of force platforms: a new method for the robust estimation of the calibration matrix.

The paper provides a new technique based on a least-squares approach for the accurate estimation of a force platform calibration matrix using simple manual procedures, when the direction of the applied loads cannot be perfectly aligned with the axes of the platform. This new procedure can be applied to all force platforms and allows the combined application of vertical and horizontal forces, both static and time-varying. The robust calibration method includes the angular errors in the least-squares parameter vector, thus reducing the bias in the estimated calibration matrix parameters. The performance of the robust method was compared with the conventional one, using a numerical simulation approach starting from a known calibration matrix. With the conventional approach, in noiseless conditions, the maximum error due to load misalignment (SD = 3 degrees) was 6% for the direct terms and over 10% for the cross-talk terms. With the robust method, these errors reduced to zero and were always below 0.4%, even when realistic noise was superimposed on the measures. With perfectly aligned loads and realistic output noise, the confidence intervals of the calibration matrix parameters were very similar for the two methods, demonstrating that the increased number of parameters did not affect the reliability of the estimate.

Biomechanical Phenomena↗
PubMed2001

Robust image registration for functional magnetic resonance imaging of the brain.

Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5 degrees in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation.

Algorithms↗
PubMed2004

A robust tolerance design method based on process capability.

This paper presents a method for robust tolerance design in terms of Process Capability Indices (PCI). The component tolerance and the suitable manufacturing processes can be selected based on the real manufacturing context. The robustness of design feasibility under the effect of uncertainties is also discussed. A comparison between the results obtained by the proposed model and other methods indicates that robust and reliable tolerance can be obtained.

Algorithms↗
PubMed2005

Robust allele-specific polymerase chain reaction markers developed for single nucleotide polymorphisms in expressed barley sequences.

Many methods have been developed to assay for single nucleotide polymorphisms (SNPs), but generally these depend on access to specialised equipment. Allele-specific polymerase chain reaction (AS-PCR) is a method that does not require specialised equipment (other than a thermocycler), but there is a common perception that AS-PCR markers can be unreliable. We have utilised a three primer AS-PCR method comprising of two flanking-primers combined with an internal allele-specific primer. We show here that this method produces a high proportion of robust markers (from candidate allele specific primers). Forty-nine inter-varietal SNP sites in 31 barley (Hordeum vulgare L.) genes were targeted for the development of AS-PCR assays. The SNP sites were found by aligning barley expressed sequence tags from public databases. The targeted genes correspond to cDNAs that have been used as restriction fragment length polymorphic probes for linkage mapping in barley. Two approaches were adopted in developing the markers. In the first approach, designed to maximise the successful development of markers to a SNP site, markers were developed for 18 sites from 19 targeted (95% success rate). With the second approach, designed to maximise the number of markers developed per primer synthesised, markers were developed for 18 SNP sites from 30 that were targeted (a 60% success rate). The robustness of markers was assessed from the range of annealing temperatures over which the PCR assay was allele-specific. The results indicate that this form of AS-PCR is highly successful for the development of robust SNP markers.

Alleles↗
PubMed2004

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis.

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.

Journal Article↗
PubMed2005

Robust and non-parametric statistics in the evaluation of figures of merit of analytical methods. Practices for students.

A set of laboratory practices is proposed in which evaluation of the quality of the analytical measurements is incorporated explicitly by applying systematically suitable methodology for extracting the useful information contained in chemical data. Non-parametric and robust techniques useful for detecting outliers have been used to evaluate different figures of merit in the validation and optimization of analytical methods. In particular, they are used for determination of the capability of detection according to ISO 11843 and IUPAC and for determination of linear range, for assessment of the response surface fitted using an experimental design to optimize an instrumental technique, and for analysis of a proficiency test carried out by different groups of students. The tools used are robust regression, least median of squares (LMS) regression, and some robust estimators as median absolute deviation (m.a.d.) or Huber estimator, which are very useful as an alternatives to the usual centralization and dispersion estimators.

Journal Article↗
PubMed1998

Robust circadian rhythmicity of Drosophila melanogaster requires the presence of lateral neurons: a brain-behavioral study of disconnected mutants.

Mutations at the disconnected (disco) locus of Drosophila melanogaster disrupt neural cell patterning in the visual system, leading to the loss of many optic lobe neurons. Drosophila's presumptive circadian pacemaker neurons--the dorsal and ventral lateral neurons--are usually among the missing cells, and most disco flies are behaviorally arrhythmic. In this study, I show that ventral lateral neurons (LNvs) are occasionally present and provoke robust circadian rhythmicity in disco mutants. Of 357 individual disco flies four animals with robust circadian rhythmicity were found. All four retained LNvs together with terminals in the superior protocerebrum. Residual or bi-circadian rhythmicity was found in about 20% of all flies; the remaining flies were completely arrhythmic. One of the flies with residual rhythmicity and two of the arrhythmic flies also had some LNvs stained. However, these flies lacked the LNv fibers in the superior protocerebrum. The results suggest that the presence of single LNvs is sufficient to provoke robust circadian rhythmicity in locomotor activity if the LNv terminals reach the superior protocerebrum. The presence of residual or bi-circadian rhythmicity in 20% of the flies without LNvs indicates that also other cells contribute to the rhythmic control of locomotor activity.

Animals↗
PubMed2005

Robust sound classification through the representation of similarity using response fields derived from stimuli during early experience.

Models of auditory processing, particularly of speech, face many difficulties. Included in these are variability among speakers, variability in speech rate, and robustness to moderate distortions such as time compression. We constructed a system based on ensembles of feature detectors derived from fragments of an onset-sensitive sound representation. This method is based on the idea of 'spectro-temporal response fields' and uses convolution to measure the degree of similarity through time between the feature detectors and the stimulus. The output from the ensemble was used to derive segmentation cues and patterns of response, which were used to train an artificial neural network (ANN) classifier. This allowed us to estimate a lower bound for the mutual information between the class of the input and the class of the output. Our results suggest that there is significant information in the output of our system, and that this is robust with respect to the exact choice of feature set, time compression in the stimulus, and speaker variation. In addition, the robustness to time compression in the stimulus has features in common with human psychophysics. Similar experiments using feature detectors derived from fragments of non-speech sounds performed less well. This result is interesting in the light of results showing aberrant cortical development in animals exposed to impoverished auditory environments during the developmental phase.

Acoustic Stimulation↗
PubMed2006

Application of robust statistical methods for sensitivity analysis of health-related quality of life outcomes.

BACKGROUND: Researchers often use conventional parametric procedures to test hypotheses of health-related quality of life (HRQL) mean equality across patient groups. However, these techniques are sensitive to the presence of skewed distributions and unequal group variances, which may characterize many HRQL measures. PURPOSE: To conduct a sensitivity analysis of conventional and robust approaches to test hypotheses of mean equality on HRQL measures for hematopoietic stem cell transplantation survivors and a healthy comparison group. METHODS: The methods applied were the conventional parametric procedure of least-squares analysis of variance applied to the raw scores, the conventional parametric procedure applied to transformed data, and a robust approximate degrees of freedom parametric procedure utilizing trimmed means and Winsorized variances. RESULTS: The choice of analysis method affected the conclusions about the null hypothesis of mean equality. More commonly observed, however, was a substantial difference in the value of the F-statistic and standard errors which was particularly evident in the measures with greater degrees of skewness and heterogeneity of variances. CONCLUSIONS: Robust statistical tests should be incorporated into sensitivity analyses when analyzing HRQL data.

Adult↗
PubMed1992

LMSMVE: a program for least median of squares regression and robust distances.

The program LMSMVE performs robust regression analysis by using the method of the least median of squares. It also computes robust distances to locate leverage points, that is, outliers with respect to the set of independent variables. LMSMVE constructs plots of least median of squares residuals against robust distances. Both methods can tolerate up to half the data being outliers before they fail to give results that describe the bulk of the data. A complete system that operates directly on SYSTAT files is available for the IBM PC and compatibles; it includes a utility that converts ASCII files to SYSTAT format.

Animals↗
PubMed1996

Merging Back-propagation and Hebbian Learning Rules for Robust Classifications.

By imposing saturation requirements on hidden-layer neural activations, a new learning algorithm is developed to improve robustness on classification performance of a multi-layer Perceptron. Derivatives of the sigmoid functions at hidden-layers are added to the standard output error with relative significance factors, and the total error is minimized by the steepest-descent method. The additional gradient-descent terms become Hebbian, and this new algorithm merges two popular learning algorithms, i.e., error back-propagation and Hebbian learning rules. Only slight modifications are needed for the standard back-propagation algorithm, and additional computational requirements are negligible. This saturation requirement effectively reduces output sensitivity to the input, which results in improved robustness and better generalization for classifier networks. Also distributed representations at hidden-layers are successfully suppressed to accomplish efficient utilization of hidden neurons. Computer simulations demonstrates much faster learning convergence as well as improved robustness for classifications and hetero-associations of binary patterns. Copyright 1996 Elsevier Science Ltd

Journal Article↗