TESTING STATISTICAL HYPOTHESIS ON RANDOM TREES AND APPLICATIONS TO THE PROTEIN CLASSIFICATION PROBLEM

Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statistical approach to test if two sets of protein domain sequences coming from two families of the Pfam database are significantly different. We model protein sequences as realizations of Variable Length Markov Chains (VLMC) and we use the context trees as a signature of each protein family. Our approach is based on a Kolmogorov-Smirnov-type goodness-of-fit test proposed by Balding et at. [Limit theorems for sequences of random trees (2008), DOI: 10.1007/s11749-008-0092-z]. The test statistic is a supremum over the space of trees of a function of the two samples; its computation grows, in principle, exponentially fast with the maximal number of nodes of the potential trees. We show how to transform this problem into a max-flow over a related graph which can be solved using a Ford-Fulkerson algorithm in polynomial time on that number. We apply the test to 10 randomly chosen protein domain families from the seed of Pfam-A database (high quality, manually curated families). The test shows that the distributions of context trees coming from different families are significantly different. We emphasize that this is a novel mathematical approach to validate the automatic clustering of sequences in any context. We also study the performance of the test via simulations on Galton-Watson related processes.

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Inference for a skew extension of the Grubbs model

In this paper, we discuss inferential aspects for the Grubbs model when the unknown quantity x (latent response) follows a skew-normal distribution, extending early results given in Arellano-Valle et al. (J Multivar Anal 96:265-281, 2005b). Maximum likelihood parameter estimates are computed via the EM-algorithm. Wald and likelihood ratio type statistics are used for hypothesis testing and we explain the apparent failure of the Wald statistics in detecting skewness via the profile likelihood function. The results and methods developed in this paper are illustrated with a numerical example.

Methods for Equivalence and Noninferiority Testing

Classical hypothesis testing focuses on testing whether treatments have differential effects on outcome. However, sometimes clinicians may be more interested in determining whether treatments are equivalent or whether one has noninferior outcomes. We review the hypotheses for these noninferiority and equivalence research questions, consider power and sample size issues, and discuss how to perform such a test for both binary and survival outcomes. The methods are illustrated on 2 recent studies in hematopoietic cell transplantation.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Statistical analysis of proficiency testing results under elliptical distributions

The use of inter-laboratory test comparisons to determine the performance of individual laboratories for specific tests (or for calibration) [ISO/IEC Guide 43-1, 1997. Proficiency testing by interlaboratory comparisons - Part 1: Development and operation of proficiency testing schemes] is called Proficiency Testing (PT). In this paper we propose the use of the generalized likelihood ratio test to compare the performance of the group of laboratories for specific tests relative to the assigned value and illustrate the procedure considering an actual data from the PT program in the area of volume. The proposed test extends the test criteria in use allowing to test for the consistency of the group of laboratories. Moreover, the class of elliptical distributions are considered for the obtained measurements. (C) 2008 Elsevier B.V. All rights reserved.

Treadmill exercise testing of asymptomatic men and women without evidence of heart disease

The aim of this study was to test the hypothesis of differences in performance including differences in ST-T wave changes between healthy men and women submitted to an exercise stress test. Two hundred (45.4%) men and 241 (54.6%) women (mean age: 38.7 ± 11.0 years) were submitted to an exercise stress test. Physiologic and electrocardiographic variables were compared by the Student t-test and the chi-square test. To test the hypothesis of differences in ST-segment changes, data were ranked with functional models based on weighted least squares. To evaluate the influence of gender and age on the diagnosis of ST-segment abnormality, a logistic model was adjusted; P < 0.05 was considered to be significant. Rate-pressure product, duration of exercise and estimated functional capacity were higher in men (P < 0.05). Sixteen (6.7%) women and 9 (4.5%) men demonstrated ST-segment upslope ≥0.15 mV or downslope ≥0.10 mV; the difference was not statistically significant. Age increase of one year added 4% to the chance of upsloping of segment ST ≥0.15 mV or downsloping of segment ST ≥0.1 mV (P = 0.03; risk ratio = 1.040, 95% confidence interval (CI) = 1.002-1.080). Heart rate recovery was higher in women (P < 0.05). The chance of women showing an increase of systolic blood pressure ≤30 mmHg was 85% higher (P = 0.01; risk ratio = 1.85, 95%CI = 1.1-3.05). No significant difference in the frequency of ST-T wave changes was observed between men and women. Other differences may be related to different physical conditioning.

TESTING THE DISTANCE-DUALITY RELATION WITH GALAXY CLUSTERS AND TYPE Ia SUPERNOVAE

In this Letter, we propose a new and model-independent cosmological test for the distance-duality (DD) relation, eta = D(L)(z)(1 + z)(-2)/D(A)(z) = 1, where D(L) and D(A) are, respectively, the luminosity and angular diameter distances. For D(L) we consider two sub-samples of Type Ia supernovae (SNe Ia) taken from Constitution data whereas D(A) distances are provided by two samples of galaxy clusters compiled by De Filippis et al. and Bonamente et al. by combining Sunyaev-Zeldovich effect and X-ray surface brightness. The SNe Ia redshifts of each sub-sample were carefully chosen to coincide with the ones of the associated galaxy cluster sample (Delta z < 0.005), thereby allowing a direct test of the DD relation. Since for very low redshifts, D(A)(z) approximate to D(L)(z), we have tested the DD relation by assuming that. is a function of the redshift parameterized by two different expressions: eta(z) = 1 + eta(0)z and eta(z) = 1 +eta(0)z/(1 + z), where eta(0) is a constant parameter quantifying a possible departure from the strict validity of the reciprocity relation (eta(0) = 0). In the best scenario (linear parameterization), we obtain eta(0) = -0.28(-0.44)(+0.44) (2 sigma, statistical + systematic errors) for the De Filippis et al. sample (elliptical geometry), a result only marginally compatible with the DD relation. However, for the Bonamente et al. sample (spherical geometry) the constraint is eta(0) = -0.42(-0.34)(+0.34) (3 sigma, statistical + systematic errors), which is clearly incompatible with the duality-distance relation.

Trigeminal pain and quantitative sensory testing in painful peripheral diabetic neuropathy

Objective: To evaluate patients with Diabetes Mellitus type 2 and painful peripheral neuropathy in order to investigate oral complaints and facial somatosensory findings. Research design and methods: Case-control study; 29 patients (12 women, mean age 57.86 yo) with Diabetes Mellitus type 2 and 31 age-gender-matched controls were evaluated with a standardized protocol for general characteristics, orofacial pain, research diagnostic criteria for temporomandibular disorders, visual analogue scale and McGill Pain questionnaire, and a systematic protocol of quantitative sensory testing for bilateral facial sensitivity at the areas innervated by the trigeminal branches, which included the thermal detection by ThermoSensi 2, tactile evaluation with vonFrey filaments, and superficial pain thresholds with a superficial algometer (Micromar). Statistical analysis was performed with Wilcoxon, chi-square, confidence intervals and Spearman (p < 0.05). Results: Orofacial pain was reported by 55.2% of patients, and the most common descriptor was fatigue (50%); 17.2% had burning mouth. Myofascial temporomandibular disorders were diagnosed in 9(31%) patients. The study group showed higher sensory thresholds of pain at the right maxillary branch (p = 0.017) but sensorial differences were not associated with pain (p = 0.608). Glycemia and HbA(1c) were positively correlated with the quantitative sensory testing results of pain (p < 0.05) and cold (p = 0.044) perceptions. Higher pain thresholds were correlated with higher glycemia and glycated hemoglobin (p = 0.027 and p = 0.026). Conclusions: There was a high prevalence of orofacial pain and burning mouth was the most common complaint. The association of loss of pain sensation and higher glycemia and glycated hemoglobin can be of clinical use for the follow-up of DM complications. (C) 2010 Elsevier Ltd. All rights reserved.

Influence of the bonding substrate in dental composite polymerization stress testing

Our objective was to compare the polymerization stress (sigma(pol)) of a series of composites obtained using poly(methyl methacrylate) (PMMA) or glass as bonding substrates, and to compare the results with those from in vitro microleakage of composite restorations. The tested hypothesis was that stress values obtained in a less rigid testing system (i.e. using PMMA) would show a better relationship with microleakage data. Five dental composites were tested: Filtek Z250 (FZ), Z100 (Z1), Concept (CO), Durafill (DU) and Heliomolar (HM). sigma(pol) was determined in 1 mm high specimens inserted between two rods (empty set = 5 mm) of either PMMA or glass. The composite elastic modulus (E) was obtained by three-point bending. sigma(pol) and E data were submitted to a one-way analysis of variance/Tukey test (alpha = 0.05). For the microleakage test (MI), bovine incisors received cylindrical cavities (empty set = 5 mm, h = 2 mm), which were restored in bulk. After storage for 24 h in water, specimens were subjected to dye penetration using AgNO(3) as tracer. Specimens were sectioned twice, perpendicularly, and microleakage was measured (in millimeters) under 20x magnification. Data from MI were submitted to the Kruskal-Wallis test. Means (SD) of sigma(pol) (MPa) using glass/PMMA were FZ: 7.5(1.8)(A)/2.5(0.2)(bc); Z1: 7.3(0.5)(A)/2.8(0.3)(ab); CO: 6.8(1.1)(A)/3.2(0.5)(a); DU: 4.5(0.7)(B)/2.0(0.2)(bc); HM: 3.5(0.2)(B)/2.3(0.3)(c). sigma(pol) obtained using PMMA rods were 34-67% lower than with glass. Means (SD) for tooth average/tooth maximum microleakage were FZ: 0.92(0.19)(B)/1.53(0.30)(a); Z1: 1.19(0.21)(A)/1.75(0.20)(a); CO: 1.26(0.25)(A)/1.78(0.24)(a); DU: 0.83(0.30)(B)/1.68(0.46)(a): HM: 0.81(0.27)(B)/1.64(0.54)(a). The tested hypothesis was confirmed, as the composites showed the same ordering both in the polymerization stress test using PMMA rods and in the microleakage test. (C) 2009 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Mechanical Testing of Indirect Composite Materials Directly Applied on Implant Abutments

Purpose: To test the strength to failure and fracture mode of three indirect composite materials directly applied onto Ti-6Al-4V implant abutments vs cemented standard porcelain-fused-to-metal (PFM) crowns. Materials and Methods: Sixty-four locking taper abutments were randomly allocated to four groups and were cleaned in ethanol in an ultrasonic bath for 5 min. After drying under ambient conditions, the abutments were grit blasted and a custom 4-cusp molar crown mold was utilized to produce identical crowns (n = 16 per group) of Tescera (Bisco), Ceramage (Shofu), and Diamond Crown (DRM) according to the manufacturer`s instructions. The porcelain-fused-to-metal crowns were fabricated by conventional means involving the construction and a wax pattern and casting of a metallic coping followed by sintering of increasing layers of porcelain. All crowns were loaded to failure by an indenter placed at one of the cusp tips at a 1 mm/min rate. Subsequently, fracture analysis was performed by means of stereomicroscopy and scanning electron microscopy. One-way ANOVA at 95% level of significance was utilized for statistical analysis. Results: The single load to failure (+/- SD) results were: Tescera (1130 +/- 239 N), Ceramage (1099 +/- 257 N), Diamond Crown (1155 +/- 284 N), and PFM (1081 +/- 243 N). Stereomicroscopy analysis showed two distinct failure modes, where the loaded cusp failed either with or without abutment/metallic coping exposure. SEM analysis of the fractures showed multiple crack propagation towards the cervical region of the crown below a region of plastic deformation at the indenter contact region. Conclusion: The three indirect composites and PFM systems fractured at loads higher than those typically associated with normal occlusal function. Although each material had a different composition and handling technique, no significant differences were found concerning their single load to fracture resistance among composite systems and PFM.

The comparative field body temperature among Liolaemus lizards: Testing the static and the labile hypotheses

Two competing hypotheses have been suggested to explain thermal sensitivity of lizards to environmental conditions. These are the static and the labile hypotheses. The static hypothesis posits that thermal physiology is evolutionary conservative and consequently relatively insensitive to directional selection. Contrarily, the labile hypothesis states that thermal physiology does respond readily to directional selection in some lizard taxa. In this paper, we tested both hypotheses among species of Liolaemus lizards. The genus Liolaemus is diverse with about 200 species, being broadly distributed from central Peru to Tierra del Fuego at the southern end of South America. Data of field body temperature (T(b)) from Liolaemus species were collected from the literature. Based on the distributional range of the species we also collected data of mean annual ambient temperatures. We observed that both the traditional analysis and the phylogenetic approach indicate that in the genus Liolaemus T(b) of species varies in a manner that is consistent with ecological gradient of ambient temperature. The data suggest that the thermal physiology of Liolaemus lizards is evolutionarily flexible, and that this plasticity has been partially responsible for the colonization of a wide array of thermal environments. (C) 2009 Elsevier Ltd. All rights reserved.

Hypotheses testing for structural calibration model

The main objective of this paper is to discuss maximum likelihood inference for the comparative structural calibration model (Barnett, in Biometrics 25:129-142, 1969), which is frequently used in the problem of assessing the relative calibrations and relative accuracies of a set of p instruments, each designed to measure the same characteristic on a common group of n experimental units. We consider asymptotic tests to answer the outlined questions. The methodology is applied to a real data set and a small simulation study is presented.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

The two-parameter Birnbaum-Saunders distribution has been used successfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing testing inference on the two parameters of the Birnbaum-Saunders distribution under type-II right censored samples. The likelihood ratio statistic and a recently proposed statistic, the gradient statistic, provide a convenient framework for statistical inference in such a case, since they do not require to obtain, estimate or invert an information matrix, which is an advantage in problems involving censored data. An extensive Monte Carlo simulation study is carried out in order to investigate and compare the finite sample performance of the likelihood ratio and the gradient tests. Our numerical results show evidence that the gradient test should be preferred. Further, we also consider the generalized Birnbaum-Saunders distribution under type-II right censored samples and present some Monte Carlo simulations for testing the parameters in this class of models using the likelihood ratio and gradient tests. Three empirical applications are presented. (C) 2011 Elsevier B.V. All rights reserved.