Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2 to 5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6 to 9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2 to 5 years and 6 to 9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6 to 9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.

Statistical computations with AstroGrid and the Grid

We outline our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, Astro- Grid) and the computational grid. We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We present our planned usage of the VOTechBroker in computing a huge number of n–point correlation functions from the SDSS, as well as fitting over a million CMBfast models to the WMAP data.

Statistical-dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change projections

A statistical–dynamical downscaling (SDD) approach for the regionalization of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily mean sea level pressure fields with the central point being located over Germany. Seventy-seven weather classes based on the associated CWT and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamically downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different data sets, the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes, results of SDD are compared to wind observations and to simulated Eout of purely dynamical downscaling (DD) methods. For the present climate, SDD is able to simulate realistic PDFs of 10-m wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD-simulated Eout. In terms of decadal hindcasts, results of SDD are similar to DD-simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout time series of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the Earth System Model of the Max Planck Institute (MPI-ESM) decadal prediction system. Long-term climate change projections in Special Report on Emission Scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to the results of other studies using DD methods, with increasing Eout over northern Europe and a negative trend over southern Europe. Despite some biases, it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.

An information-theoretic approach to statistical dependence: Copula information

We discuss the connection between information and copula theories by showing that a copula can be employed to decompose the information content of a multivariate distribution into marginal and dependence components, with the latter quantified by the mutual information. We define the information excess as a measure of deviation from a maximum-entropy distribution. The idea of marginal invariant dependence measures is also discussed and used to show that empirical linear correlation underestimates the amplitude of the actual correlation in the case of non-Gaussian marginals. The mutual information is shown to provide an upper bound for the asymptotic empirical log-likelihood of a copula. An analytical expression for the information excess of T-copulas is provided, allowing for simple model identification within this family. We illustrate the framework in a financial data set. Copyright (C) EPLA, 2009

Speculative bubbles and contagion: analysis of volatility's clusters during the DotCom bubble based on the dynamic conditional correlation model

Reviewing the de nition and measurement of speculative bubbles in context of contagion, this paper analyses the DotCom bubble in American and European equity markets using the dynamic conditional correlation (DCC) model proposed by (Engle and Sheppard 2001) as on one hand as an econometrics explanation and on the other hand the behavioral nance as an psychological explanation. Contagion is de ned in this context as the statistical break in the computed DCCs as measured by the shifts in their means and medians. Even it is astonishing, that the contagion is lower during price bubbles, the main nding indicates the presence of contagion in the di¤erent indices among those two continents and proves the presence of structural changes during nancial crisis

Fractal correlation of heart rate variability in obese children

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of estrogen receptor alpha and beta and progesterone receptor expression and correlation with clinicopathologic factors and proliferative marker Ki-67 in breast cancers

To elucidate the molecular profile of hormonal steroid receptor status, we analyzed ER-alpha, ER-beta, and PGR mRNA and protein expression in 80 breast carcinomas using reverse transcriptase polymerase chain reaction (RT-PCR), quantitative RT-PCR, and immunohistochemical analysis. Qualitative analysis revealed positive expression of ER-alpha, ER-beta, and PGR mRNA in 48%, 59%, and 48% of the breast carcinomas, respectively. ER-alpha, ER-beta, and PGR transcript overexpression was observed in 51%, 0%, and 12% of the cases, respectively, whereas moderate or strong protein expression was detected in 68%, 78%, and 49% of the cases, respectively. Tumor grade was negatively correlated with transcript and protein levels of ER-alpha (P = .0169 and P = .0006, respectively) and PGR (P = .0034 and P = .0005, respectively). Similarly, proliferative index Ki-67 was negatively associated with transcript and protein levels of ER-alpha (P = .0006 and P < .0001, respectively) and PGR (P = .0258 and P =. 0005, respectively). These findings suggest that ER-alpha and PGR expression are associated with well-differentiated breast tumors and less directly related to cell proliferation. A significant statistical difference was observed between lymph node status and ER-beta protein expression (P = .0208). In ER-alpha-negative tumors, we detected a correlation between ER-beta protein expression and high levels of Ki-67. These data suggest that ER-beta could be a prognostic marker in human breast cancer. (C) 2008 Elsevier B.V. All rights reserved.

Anatomic study of the mandibular foramen, lingula and antilingula in dry mandibles, and its statistical relationship between the true lingula and the antilingula

The authors verified the anatomical location of the mandibular foramen, lingula and antilingula in dry mandibles, aiming to obtain information that could be used when performing mandibular osteotomies. Forty-four mandibles (88 sides) were evaluated. The distances were measured using a sliding calliper, with the mandibles fixed in a reproducible position. Results showed that the mandibular foramen is on average 5.82 mm below the lingula. Regarding the statistical comparison between the mandibular foramen entrance and the anti lingula position, there is no correlation between the position of those two structures in the studied sample. The mandibular foramen is slightly posterior in relation to the centre of the ramus. The lingula is an important anatomic landmark for ramus surgery, and for determining the distance to the mandibular foramen entrance. The use of the antilingula as a landmark for the position of the vertical ramus osteotomy is not recommended.

Apoptosis in tongue squamous cell carcinoma and its correlation with clinically occult cervical metastasis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Correlation times in stochastic equations with delayed feedback and multiplicative noise

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Residual signal auto-correlation to evaluate speech in Parkinson's disease patients

Objective: To evaluate the maximum residual signal auto-correlation also known as pitch amplitude (PA) values in patients with Parkinson's disease (PD) patients. Method. The signals of 21 Parkinson's patients were compared with 15 healthy individuals, divided according age and gender. Results: Statistical difference was seen between groups for PA, 0.39 for controls and 0.25 for PD. Normal value threshold was set as 0.3; (p <= 0.001). In the Parkinson's group 80.77%, and in the control group only 12.28%, had a PA < 0.3 demonstrating an association between these variables. The dispersion diagram for age and PA for PD individuals showed p=0.01 and r=0.54. There was no significant difference in relation to gender and PA between groups: Conclusion: the significant differences in pitch's amplitude between PD patients and healthy individuals demonstrate the methods specificity.-The results showed the need of prospective controlled studies,to improve the use and indications of residual signal auto-correlation to evaluate speech in PD patients.

Auditory processing disorder in patients with language-learning impairment and correlation with malformation of cortical development

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Serology of paracoccidioidomycosis. II. Correlation between class-specific antibodies and clinical forms of the disease

Untreated and previously treated patients with paracoccidioidomycosis were studied for: (i) serum levels of total IgG, IgM and IgA immunoglobulins, by radial immunodiffusion and Paracoccidioides brasiliensis (Pb) antibodies, by indirect immunofluorescence; (ii) correlation between their levels with the clinical forms of the disease; (iii) correlation between the serum titres obtained by tube precipitin with those of anti-Pb IgG, IgM and IgA. In the untreated group, serum IgG levels were significantly increased in patients with the more systemic forms of the disease, especially the acute progressive form. Serum IgA levels were significantly increased in all patients with no statistical difference between clinical forms. Serum IgM levels were normal in all patients. Anti-Pb IgG, IgA and IgM were detected in 97·5%, 32·5% and 45·0% of all cases, respectively. There was a sharp tendency towards higher levels of anti-Pb IgG among those with the acute progressive form (83·4%) in relation to the chronic, more localized forms, mixed form (68·0%) and isolated organic form (55·5%). In the untreated and previously treated group sera, there was positive correlation between the level of anti-Pb IgG and positivity for the tube precipitin test, suggesting that the precipitin-type antibodies are of the IgG class. Broadly, the present data demonstrate a polyclonal activation of the humoral immune system in paracoccidioidomycosis, with a positive relationship between serological results and severity of the disease. © 1984.

Anaerobic threshold estimation by statistical modelling.

Anaerobic threshold (AT) is usually estimated as a change point problem by visual analysis of the cardiorespiratory response to incremental dynamic exercise. In this study, two phase linear (TPL) models of the linear-linear and linear-quadratic type were used for the estimation of AT. The correlation coefficient between the classical and statistical approaches was 0.88, and 0.89 after outlier exclusion. The TPL models provide a simple method for estimating AT that can be easily implemented using a digital computer for the automatic pattern recognition of AT.