Asymptotic robust inferences in the analysis of mean and covariance structures

Structural equation models are widely used in economic, socialand behavioral studies to analyze linear interrelationships amongvariables, some of which may be unobservable or subject to measurementerror. Alternative estimation methods that exploit different distributionalassumptions are now available. The present paper deals with issues ofasymptotic statistical inferences, such as the evaluation of standarderrors of estimates and chi--square goodness--of--fit statistics,in the general context of mean and covariance structures. The emphasisis on drawing correct statistical inferences regardless of thedistribution of the data and the method of estimation employed. A(distribution--free) consistent estimate of $\Gamma$, the matrix ofasymptotic variances of the vector of sample second--order moments,will be used to compute robust standard errors and a robust chi--squaregoodness--of--fit squares. Simple modifications of the usual estimateof $\Gamma$ will also permit correct inferences in the case of multi--stage complex samples. We will also discuss the conditions under which,regardless of the distribution of the data, one can rely on the usual(non--robust) inferential statistics. Finally, a multivariate regressionmodel with errors--in--variables will be used to illustrate, by meansof simulated data, various theoretical aspects of the paper.

Homologous DNA pairing domain peptides of RecA protein: intrinsic propensity to form beta-structures and filaments.

The 20 amino acid residue peptides derived from RecA loop L2 have been shown to be the pairing domain of RecA. The peptides bind to ss- and dsDNA, unstack ssDNA, and pair the ssDNA to its homologous target in a duplex DNA. As shown by circular dichroism, upon binding to DNA the disordered peptides adopt a beta-structure conformation. Here we show that the conformational change of the peptide from random coil to beta-structure is important in binding ss- and dsDNA. The beta-structure in the DNA pairing peptides can be induced by many environmental conditions such as high pH, high concentration, and non-micellar sodium dodecyl sulfate (6 mM). This behavior indicates an intrinsic property of these peptides to form a beta-structure. A beta-structure model for the loop L2 of RecA protein when bound to DNA is thus proposed. The fact that aromatic residues at the central position 203 strongly modulate the peptide binding to DNA and subsequent biochemical activities can be accounted for by the direct effect of the aromatic amino acids on the peptide conformational change. The DNA-pairing domain of RecA visualized by electron microscopy self-assembles into a filamentous structure like RecA. The relevance of such a peptide filamentous structure to the structure of RecA when bound to DNA is discussed.

Tertiary Himalayan structures and metamorphism in the Kulu Valley (Mandi-Khoksar transect of the Western Himalaya) - Shikar-Beh-nappe and crystalline nappe

The Crystalline Nappe of the High Himalayan Crystalline has been examined along the Kulu Valley and its vicinity (Mandi-Khoksar transect). This nappe was believed to have undergone deformation related only to its transport towards the SW essentially during the `'Main Central Thrust event''. New data has led to the conclusion that during the Himalayan orogeny, two distinctive phases, related to two opposite transport directions, characterize the evolution of this part of the chain, before the creation of the late NE-vergent backfolding. The first phase corresponds to an early NE-vergent folding and thrusting, creating the Tandi Syncline and the NE-oriented Shikar Beh Nappe stack, with a displacement amplitude of about 50 km. Two schistosities, together with a strong stretching lineation are developed at a deep tectonic level under amphibolite facies conditions (kyanite-staurolite-garnet-two mica schists). At a higher tectonic level and in the southern part of the section (Tandy Syncline and southern Kulu Valley between Kulu and Mandi) one or two schistosities are developed in the greenschist facies grade rocks (garnet-biotite and biotite schists). These structures and the associated Barrovian type metamorphism are all related to the NE-verging Shikar Beh Nappe. The creation of the NE-verging Shikar Beh Nappe may be explained by the reactivation of a SW dipping listric normal fault of the N Indian flexural passive margin, during the early stages of the Himalayan orogeny. In the second phase, the still hot metamorphic rocks of the Shikar Beh Nappe were folded and thrust towards the SW (mainly along the MBT and the MCT with a displacement in excess of 100 km) onto the cold, low-grade metamorphic rocks of the Larji-Kulu-Rampur Window or, near Mandi, on the non-metamorphic sandstones of the Ganges Molasse (Siwaliks). Sense of shear criteria and a strong NE-SW stretching-lineation indicate that the Crystalline Nappe has been overthrusted towards the SW. Thermometry on synkinematically crystallised garnet-biotite and garnet-hornblende pairs reveals the lower amphibolite facies temperature conditions related to the Crystalline Nappe formation. From the muscovite and biotite Rb-Sr cooling ages, the Shikar Beh Nappe emplacement occurred before 32 Ma and the southwestward thrusting of the Crystalline Nappe began before 21 Ma. Our model involving two opposite directions of thrusting goes against the conventional idea of only one main SW-oriented transport direction in the High Himalayan Crystalline Nappes.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Properties of non-crystalline EuIG and DyIG obtained from Mossbauer and magnetization data

Non crystalline (nc) EuIG and DyIG have been prepared by dc¿sputtering. Mössbauer data on 57Fe, 151Eu and 161Dy reveal sharp magnetic transitions at 62 K and 70 K for nc EuIG and DyIG, respectively. The 57Fe hyperfine (hf) spectra consist of three superpositioned patterns for Fe3+ in tetrahedral and octahedral and for Fe2+ in tetrahedral oxygen coordination. The saturation hf fields at 4.2 K are reduced compared to the values of the corresponding crystalline materials. The induced hf field at 151Eu is only 1/8 of that for crystalline EuIG

Non-Destructive Evaluation of Iowa Pavements Phase 2: Development of a Fully Automated Software System for Rapid Analysis/Processing of the Falling Weight Deflectometer Data, 2009

The Office of Special Investigations at Iowa Department of Transportation (DOT) collects FWD data on regular basis to evaluate pavement structural conditions. The primary objective of this study was to develop a fully-automated software system for rapid processing of the FWD data along with a user manual. The software system automatically reads the FWD raw data collected by the JILS-20 type FWD machine that Iowa DOT owns, processes and analyzes the collected data with the rapid prediction algorithms developed during the phase I study. This system smoothly integrates the FWD data analysis algorithms and the computer program being used to collect the pavement deflection data. This system can be used to assess pavement condition, estimate remaining pavement life, and eventually help assess pavement rehabilitation strategies by the Iowa DOT pavement management team. This report describes the developed software in detail and can also be used as a user-manual for conducting simulation studies and detailed analyses. *********************** Large File ***********************

Photodynamic Diagnosis of Non-muscle-invasive Bladder Cancer with Hexaminolevulinate Cystoscopy: A Meta-analysis of Detection and Recurrence Based on Raw Data.

BACKGROUND: Studies on hexaminolevulinate (HAL) cystoscopy report improved detection of bladder tumours. However, recent meta-analyses report conflicting effects on recurrence. OBJECTIVE: To assess available clinical data for blue light (BL) HAL cystoscopy on the detection of Ta/T1 and carcinoma in situ (CIS) tumours, and on tumour recurrence. DESIGN, SETTING, AND PARTICIPANTS: This meta-analysis reviewed raw data from prospective studies on 1345 patients with known or suspected non-muscle-invasive bladder cancer (NMIBC). INTERVENTION: A single application of HAL cystoscopy was used as an adjunct to white light (WL) cystoscopy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We studied the detection of NMIBC (intention to treat [ITT]: n=831; six studies) and recurrence (per protocol: n=634; three studies) up to 1 yr. DerSimonian and Laird's random-effects model was used to obtain pooled relative risks (RRs) and associated 95% confidence intervals (CIs) for outcomes for detection. RESULTS AND LIMITATIONS: BL cystoscopy detected significantly more Ta tumours (14.7%; p<0.001; odds ratio [OR]: 4.898; 95% CI, 1.937-12.390) and CIS lesions (40.8%; p<0.001; OR: 12.372; 95% CI, 6.343-24.133) than WL. There were 24.9% patients with at least one additional Ta/T1 tumour seen with BL (p<0.001), significant also in patients with primary (20.7%; p<0.001) and recurrent cancer (27.7%; p<0.001), and in patients at high risk (27.0%; p<0.001) and intermediate risk (35.7%; p=0.004). In 26.7% of patients, CIS was detected only by BL (p<0.001) and was also significant in patients with primary (28.0%; p<0.001) and recurrent cancer (25.0%; p<0.001). Recurrence rates up to 12 mo were significantly lower overall with BL, 34.5% versus 45.4% (p=0.006; RR: 0.761 [0.627-0.924]), and lower in patients with T1 or CIS (p=0.052; RR: 0.696 [0.482-1.003]), Ta (p=0.040; RR: 0.804 [0.653-0.991]), and in high-risk (p=0.050) and low-risk (p=0.029) subgroups. Some subgroups had too few patients to allow statistically meaningful analysis. Heterogeneity was minimised by the statistical analysis method used. CONCLUSIONS: This meta-analysis confirms that HAL BL cystoscopy significantly improves the detection of bladder tumours leading to a reduction of recurrence at 9-12 mo. The benefit is independent of the level of risk and is evident in patients with Ta, T1, CIS, primary, and recurrent cancer.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Quantile Estimation in Non-Stationary Streaming Data

We provide an incremental quantile estimator for Non-stationary Streaming Data. We propose a method for simultaneous estimation of multiple quantiles corresponding to the given probability levels from streaming data. Due to the limitations of the memory, it is not feasible to compute the quantiles by storing the data. So estimating the quantiles as the data pass by is the only possibility. This can be effective in network measurement. To provide the minimum of the mean-squared error of the estimation, we use parabolic approximation and for comparison we simulate the results for different number of runs and using both linear and parabolic approximations.

Méta-analyses d'essais randomisés sur données individuelles dans le traitement des cancers ORL non métastatiques : principes, résultats, perspectives [Individual patient data meta-analyses of randomized trials for the treatment of non-metastatic head and neck squamous cell carcinomas: Principles, results and perspectives].

Meta-analyses are considered as an important pillar of evidence-based medicine. The aim of this review is to describe the main principles of a meta-analysis and to use examples of head and neck oncology to demonstrate their clinical impact and methodological interest. The major role of individual patient data is outlined, as well as the superiority of individual patient data over meta-analyses based on published summary data. The major clinical breakthrough of head and neck meta-analyses are summarized, regarding concomitant chemotherapy, altered fractionated chemotherapy, new regimens of induction chemotherapy or the use of radioprotectants. Recent methodological developments are described, including network meta-analyses, the validation of surrogate markers. Lastly, the future of meta-analyses is discussed in the context of personalized medicine.

Data Envelopment Analysis and non-discretionary inputs : How to select the most suitable model using multi-criteria decision analysis

Within Data Envelopment Analysis, several alternative models allow for an environmental adjustment. The majority of them deliver divergent results. Decision makers face the difficult task of selecting the most suitable model. This study is performed to overcome this difficulty. By doing so, it fills a research gap. First, a two-step web-based survey is conducted. It aims (1) to identify the selection criteria, (2) to prioritize and weight the selection criteria with respect to the goal of selecting the most suitable model and (3) to collect the preferences about which model is preferable to fulfil each selection criterion. Second, Analytic Hierarchy Process is used to quantify the preferences expressed in the survey. Results show that the understandability, the applicability and the acceptability of the alternative models are valid selection criteria. The selection of the most suitable model depends on the preferences of the decision makers with regards to these criteria.

Characteristics of 2D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS

Flood simulation studies use spatial-temporal rainfall data input into distributed hydrological models. A correct description of rainfall in space and in time contributes to improvements on hydrological modelling and design. This work is focused on the analysis of 2-D convective structures (rain cells), whose contribution is especially significant in most flood events. The objective of this paper is to provide statistical descriptors and distribution functions for convective structure characteristics of precipitation systems producing floods in Catalonia (NE Spain). To achieve this purpose heavy rainfall events recorded between 1996 and 2000 have been analysed. By means of weather radar, and applying 2-D radar algorithms a distinction between convective and stratiform precipitation is made. These data are introduced and analyzed with a GIS. In a first step different groups of connected pixels with convective precipitation are identified. Only convective structures with an area greater than 32 km2 are selected. Then, geometric characteristics (area, perimeter, orientation and dimensions of the ellipse), and rainfall statistics (maximum, mean, minimum, range, standard deviation, and sum) of these structures are obtained and stored in a database. Finally, descriptive statistics for selected characteristics are calculated and statistical distributions are fitted to the observed frequency distributions. Statistical analyses reveal that the Generalized Pareto distribution for the area and the Generalized Extreme Value distribution for the perimeter, dimensions, orientation and mean areal precipitation are the statistical distributions that best fit the observed ones of these parameters. The statistical descriptors and the probability distribution functions obtained are of direct use as an input in spatial rainfall generators.