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Doubly protected methods are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector $$\mathbf {X}$$ of covariates is observed at each individual, and the missing mechanism is assumed to be independent of the response, conditioned on $$\mathbf {X}$$ (missing at random). In recent years, many authors have turned from the estimation of the mean to that of the median, and more generally, doubly protected estimators of the quantiles have been proposed. In this work, we present doubly protected estimators for the quantiles in semiparametric models that are also robust, in the sense that they are resistant to the presence of outliers in the sample.

In this‐30 work we propose a model selection criterion to estimate the points of independence of a random vector, producing a decomposition of the vector distribution function into independent blocks. The method, based on a general estimator of the distribution function, can be applied for discrete or continuous random vectors, and for i.i.d. data or dependent time series. We prove the consistency of the approach under general conditions on the estimator of the distribution function and we show that the consistency holds for i.i.d. data and discrete time series with mixing conditions. We also propose an efficient algorithm to approximate the estimator and show the performance of the method on simulated data. We apply the method in a real dataset to estimate the distribution of the flow over several locations on a river, observed at different time points.

Generalized linear models are often assumed to fit propensity scores, which are used to compute inverse probability weighted (IPW) estimators. To derive the asymptotic properties of IPW estimators, the propensity score is supposed to be bounded away from zero. This condition is known in the literature as strict positivity (or positivity assumption), and, in practice, when it does not hold, IPW estimators are very unstable and have a large variability. Although strict positivity is often assumed, it is not upheld when some of the covariates are unbounded. In real data sets, a data-generating process that violates the positivity assumption may lead to wrong inference because of the inaccuracy in the estimations. In this work, we attempt to conciliate between the strict positivity condition and the theory of generalized linear models by incorporating an extra parameter, which results in an explicit lower bound for the propensity score. An additional parameter is added to fulfil the overlap assumption in the causal framework.

In this paper, we propose a new procedure to estimate the distribution of a variable y when there are missing data. To compensate the presence of missing responses, it is assumed that a covariate vector x is observed and that y and x are related by means of a semi-parametric regression model. Observed residuals are combined with predicted values to estimate the missing response distribution. Once the responses distribution is consistently estimated, we can estimate any parameter defined through a continuous functional T using a plug in procedure. We prove that the proposed estimators have high breakdown point. Fil: Statti, María Florencia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

In many situations, when dealing with several populations, equality of the covariance operators is assumed. An important issue is to study whether this assumption holds before making other inferences. In this paper, we develop a test for comparing covariance operators of several functional data samples. The proposed test is based on the Hilbert–Schmidt norm of the difference between estimated covariance operators. In particular, when dealing with two populations, the test statistic is just the squared norm of the difference between the two covariance operators estimators. The asymptotic behaviour of the test statistic under both the null hypothesis and local alternatives is obtained. The computation of the quantiles of the null asymptotic distribution is not feasible in practice. To overcome this problem, a bootstrap procedure is considered. The performance of the test statistic for small sample sizes is illustrated through a Monte Carlo study and on a real data set. Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors.We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct. Fil: Molina, J.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Robins, J. M.. Harvard University; Estados Unidos

The calculation of free energy profiles is central in understanding differential enzymatic activity, for instance, involving chemical reactions that require QM-MM tools, ligand migration, and conformational rearrangements that can be modeled using classical potentials. The use of steered molecular dynamics (sMD) together with the Jarzynski equality is a popular approach in calculating free energy profiles. Here, we first briefly review the application of the Jarzynski equality to sMD simulations, then revisit the so-called stiff-spring approximation and the consequent expectation of Gaussian work distributions and, finally, reiterate the practical utility of the second-order cumulant expansion, as it coincides with the parametric maximum-likelihood estimator in this scenario. We illustrate this procedure using simulations of CO, both in aqueous solution and in a carbon nanotube as a model system for biologically relevant nanoheterogeneous environments. We conclude the use of the second-order cumulant expansion permits the use of faster pulling velocities in sMD simulations, without introducing bias due to large dispersion in the non-equilibrium work distribution.

Objectives: Early life represents a sensitive and critical period for an individual. Nutrition plays a crucial role in the maturation and functional development of the central nervous system. Inadequate nutrition before birth and during the postnatal life can seriously interfere with brain development and lead to behavioral and neurological disorders such as learning disabilities and psychiatric diseases. In addition, the quality of mother–infant interactions represents an important adaptive pathway that prepares offspring for the conditions of life. In this work, we asked if protein malnutrition alters maternal care and offspring development and if these phenotypes can be transmitted to next generation. Methods: Female mice were fed with a normal or hypoproteic diet during pregnancy and lactation. Nurturing behaviors, i.e. arched, blanket and passive nursing, and liking and grooming of the pups, were evaluated from postnatal day 1 (PD1) to postnatal day 7 (PD7). The same protocol was employed to evaluate maternal behavior for filial generation 1 (F1) and filial generation 2 (F2) dams. Offspring development was evaluated for F1, F2, and F3 generations. Developmental landmarks and neurological reflexes were assessed from PD8 until complete development of the landmark or acquisition of the reflex. Results: Our results show that malnourished dams provide a lesser and more fragmented maternal care than their normally fed counterparts. This altered maternal behavior as well as the delay in the physical and neurological development observed in the offspring from malnourished mothers was transmitted up to two generations at least. Conclusion: These results highlight the harmful effects of protein malnutrition even for generations that are not directly exposed to this environmental adversity. Fil: Gianatiempo, Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Sonzogni, Silvina Veronica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Fesser, Estefanía Aylén. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Belluscio, Laura María. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Smucler, Ezequiel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentina Fil: Sued, Raquel Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Canepa, Eduardo Tomas. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina

Due to increased recording capability, functional data analysis has become an important research topic. For functional data, the study of outlier detection and/or the development of robust statistical procedures started only recently. One robust alternative to the sample covariance operator is the sample spatial sign covariance operator. In this paper, we study the asymptotic behavior of the sample spatial sign covariance operator centered at an estimated location. Among possible applications of our results, we derive the asymptotic distribution of the principal directions obtained from the sample spatial sign covariance operator and we develop a testing procedure to detect differences between the scatter operators of two populations. The test performance is illustrated through a Monte Carlo study for small sample sizes. Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina

Semi-supervised learning deals with the problem of how, if possible, to take advantage of a huge amount of unclassified data, to perform a classification in situations when, typically, there is little labeled data. Even though this is not always possible (it depends on how useful, for inferring the labels, it would be to know the distribution of the unlabeled data), several algorithm have been proposed recently. %but in general they are not proved to outperform A new algorithm is proposed, that under almost necessary conditions, %and it is proved that it attains asymptotically the performance of the best theoretical rule as the amount of unlabeled data tends to infinity. The set of necessary assumptions, although reasonable, show that semi-supervised classification only works for very well conditioned problems. The focus is on understanding when and why semi-supervised learning works when the size of the initial training sample remains fixed and the asymptotic is on the size of the unlabeled data. The performance of the algorithm is assessed in the well known "Isolet" real-data of phonemes, where a strong dependence on the choice of the initial training sample is shown., Comment: arXiv admin note: substantial text overlap with arXiv:1709.05673

Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are available for reduced-rank multivariate generalized linear models. We develop M-estimation theory for concave criterion functions that are maximized over parameter spaces that are neither convex nor closed. These results are used to derive the consistency and asymptotic distribution of maximum likelihood estimators in reduced-rank multivariate generalized linear models, when the response and predictor vectors have a joint distribution. We illustrate our results in a real data classification problem with binary covariates.

In a missing-data setting, we want to estimate the mean of a scalar outcome, based on a sample in which an explanatory variable is observed for every subject while responses are missing by happenstance for some of them. We consider two kinds of estimates of the mean response when the explanatory variable is functional. One is based on the average of the predicted values and the second one is a functional adaptation of the Horvitz-Thompson estimator. We show that the infinite dimensionality of the problem does not affect the rates of convergence by stating that the estimates are root-n consistent, under missing at random (MAR) assumption. These asymptotic features are completed by simulated experiments illustrating the easiness of implementation and the good behaviour on finite sample sizes of the method. This is the first paper emphasizing that the insensitiveness of averaged estimates, well known in multivariate non-parametric statistics, remains true for an infinite-dimensional covariable. In this sense, this work opens the way for various other results of this kind in functional data analysis. Fil: Ferraty, Frédéric. Universite Paul Sabatier. Institut de Mathematiques de Toulouse; Francia Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Vieu, Philippe. Universite Paul Sabatier. Institut de Mathematiques de Toulouse; Francia

Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory. Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella. Departamento de Economía; Argentina Fil: Lei, Quanhong. Harvard University; Estados Unidos Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Robins, James M.. Harvard University; Estados Unidos

Motivation: Hemeproteins have many diverse functions that largely depend on the rate at which they uptake or release small ligands, like oxygen. These proteins have been extensively studied using either simulations or experiments, albeit only qualitatively and one or two proteins at a time. Results: We present a physical-chemical model, which uses data obtained exclusively from computer simulations, to describe the uptake and release of oxygen in a family of hemeproteins, called truncated hemoglobins (trHbs). Through a rigorous statistical analysis we demonstrate that our model successfully recaptures all the reported experimental oxygen association and dissociation kinetic rate constants, thus allowing us to establish the key factors that determine the rates at which these hemeproteins uptake and release oxygen. We found that internal tunnels as well as the distal site water molecules control ligand uptake, whereas oxygen stabilization by distal site residues controls ligand release. Because these rates largely determine the functions of these hemeproteins, these approaches will also be important tools in characterizing the trHbs members with unknown functions. Fil: Bustamante, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina Fil: Szretter Noste, María Eugenia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina Fil: Sued, Raquel Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina Fil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: Estrin, Dario Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina Fil: Boechi, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Stochastic ordinary differential equations may have solutions that explode in finite or infinite time. In this article we design an adaptive numerical scheme that reproduces the explosive behavior. The time step is adapted according to the size of the computed solution in such a way that, under adequate hypotheses, the explosion of the solutions is reproduced.

The asymmetric mean zero simple exclusion process is an example of non-reversible nongradient system. We prove that the diffusion coefficient of its hydrodynamic equation is a C ∞ function on [ 0 , 1 ] , in all dimension.

In this work, we propose a different “surgical modified model” for the construction of counterfactual variables under non-parametric structural equation models. This approach allows the simultaneous representation of counterfactual responses and observed treatment assignment, at least when the intervention is done in one node. Using the new proposal, the d-separation criterion is used to verify conditions related with ignorability or conditional ignorability, and a new proof of the back door theorem is provided under this framework. Fil: Molina, Julieta. Universidad de Buenos Aires; Argentina Fil: Pantazis, Lucio José. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Obligate avian brood parasites lay their eggs in nests of other species (hosts), which raise parasitic young. Parasitic nestlings are likely to influence host's parental behaviours as they typically beg for food more vigorously than young host for a given hunger level. However, few studies have tested this idea, with conflicting results. These prior studies were largely limited to biparental hosts, but little is known about the effect of brood parasitism on parental behaviours in hosts that breed cooperatively. We followed a multimodel approach to examine the effect of brood parasitism on nest provisioning and helper recruitment in the baywing (Agelaioides badius), a cooperative breeder parasitised by screaming (Molothrus rufoaxillaris) and shiny (Molothrus bonariensis) cowbirds. Multimodel inference results indicated that feeding visits increased with nestling age, cooperative group size and number of cowbird nestlings in the brood. Brood size had little influence on feeding visits, which further suggests that baywings adjusted their provisioning effort in response to cowbird parasitism. In addition, nests parasitised artificially with shiny cowbird eggs or hatchlings recruited more helpers than unmanipulated nests having only host or screaming cowbird young. Our results provide novel evidence that brood parasitism and cooperative breeding interact in determining the levels of nest provisioning. Fil: Ursino, Cynthia Alejandra. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: de Marsico, Maria Cecilia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Farall, Andrés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina Fil: Reboreda, Juan Carlos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

In many situations, when dealing with several populations, equality of the covariance operators is assumed. In this work, we will study a hypothesis test to validate this assumption.

Background A century after its discovery, Chagas disease still represents a major neglected tropical threat. Accurate diagnostics tools as well as surrogate markers of parasitological response to treatment are research priorities in the field. The purpose of this study was to evaluate the performance of PCR methods in detection of Trypanosoma cruzi DNA by an external quality evaluation. Methodology/Findings An international collaborative study was launched by expert PCR laboratories from 16 countries. Currently used strategies were challenged against serial dilutions of purified DNA from stocks representing T. cruzi discrete typing units (DTU) I, IV and VI (set A), human blood spiked with parasite cells (set B) and Guanidine Hidrochloride-EDTA blood samples from 32 seropositive and 10 seronegative patients from Southern Cone countries (set C). Forty eight PCR tests were reported for set A and 44 for sets B and C; 28 targeted minicircle DNA (kDNA), 13 satellite DNA (Sat-DNA) and the remainder low copy number sequences. In set A, commercial master mixes and Sat-DNA Real Time PCR showed better specificity, but kDNA-PCR was more sensitive to detect DTU I DNA. In set B, commercial DNA extraction kits presented better specificity than solvent extraction protocols. Sat-DNA PCR tests had higher specificity, with sensitivities of 0.05–0.5 parasites/mL whereas specific kDNA tests detected 5.10−3 par/mL. Sixteen specific and coherent methods had a Good Performance in both sets A and B (10 fg/µl of DNA from all stocks, 5 par/mL spiked blood). The median values of sensitivities, specificities and accuracies obtained in testing the Set C samples with the 16 tests determined to be good performing by analyzing Sets A and B samples varied considerably. Out of them, four methods depicted the best performing parameters in all three sets of samples, detecting at least 10 fg/µl for each DNA stock, 0.5 par/mL and a sensitivity between 83.3–94.4%, specificity of 85–95%, accuracy of 86.8–89.5% and kappa index of 0.7–0.8 compared to consensus PCR reports of the 16 good performing tests and 63–69%, 100%, 71.4–76.2% and 0.4–0.5, respectively compared to serodiagnosis. Method LbD2 used solvent extraction followed by Sybr-Green based Real time PCR targeted to Sat-DNA; method LbD3 used solvent DNA extraction followed by conventional PCR targeted to Sat-DNA. The third method (LbF1) used glass fiber column based DNA extraction followed by TaqMan Real Time PCR targeted to Sat-DNA (cruzi 1/cruzi 2 and cruzi 3 TaqMan probe) and the fourth method (LbQ) used solvent DNA extraction followed by conventional hot-start PCR targeted to kDNA (primer pairs 121/122). These four methods were further evaluated at the coordinating laboratory in a subset of human blood samples, confirming the performance obtained by the participating laboratories. Conclusion/Significance This study represents a first crucial step towards international validation of PCR procedures for detection of T. cruzi in human blood samples., Author Summary A century after its discovery, Chagas disease, caused by the parasite Trypanosoma cruzi, still represents a major neglected tropical threat. Accurate diagnostics tools as well as surrogate markers of parasitological response to treatment are research priorities in the field. The polymerase chain reaction (PCR) has been proposed as a sensitive laboratory tool for detection of T. cruzi infection and monitoring of parasitological treatment outcome. However, high variation in accuracy and lack of international quality controls has precluded reliable applications in the clinical practice and comparisons of data among cohorts and geographical regions. In an effort towards harmonization of PCR strategies, 26 expert laboratories from 16 countries evaluated their current PCR procedures against sets of control samples, composed by serial dilutions of T.cruzi DNA from culture stocks belonging to different lineages, human blood spiked with parasite cells and blood samples from Chagas disease patients. A high variability in sensitivities and specificities was found among the 48 reported PCR tests. Out of them, four tests with best performance were selected and further evaluated. This study represents a crucial first step towards device of a standardized operative procedure for T. cruzi PCR.

This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S and MM estimates. A restricted type of differentiability, called weak differentiability, is defined, which suffices to prove the asymptotic normality of estimates based on the functionals. This approach allows to prove the consistency, asymptotic normality and qualitative robustness of M estimates under more general conditions than those required in standard approaches. In particular, we prove that regression MM-estimates are asymptotically normal when the observations are $\phi$-mixing., Comment: Published in at http://dx.doi.org/10.3150/11-BEJ368 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

The common principal components model for several groups of multivariate observations assumes equal principal axes among the groups. Robust estimators can be defined replacing the sample variance by a robust dispersion measure. This paper studies the asymptotic distribution of robust projection-pursuit estimators under a common principal components model. Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Molina, Fernanda Julieta. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

In a missing data setting, we have a sample in which a vector of explanatory variables xi is observed for every subject i, while scalar responses yi are missing by happenstance on some individuals. In this work we propose robust estimators of the distribution of the responses assuming missing at random (MAR) data, under a semiparametric regression model. Our approach allows the consistent estimation of any weakly continuous functional of the response’s distribution. In particular, strongly consistent estimators of any continuous location functional, such as the median, L-functionals and M-functionals, are proposed. A robust fit for the regression model combined with the robust properties of the location functional gives rise to a robust recipe for estimating the location parameter. Robustness is quantified through the breakdown point of the proposed procedure. The asymptotic distribution of the location estimators is also derived. The proofs of the theorems are presented in Supplementary Material available online. Avec les donnees manquantes, nous avons un ´ echantillon pour lequel les variables explicatives ´ xi sont observees pour chaque sujet ´ i, tandis que les variables reponses ´ yi sont manquantes au hasard pour quelques individus. Dans ce travail, nous proposons des estimateurs robustes pour la fonction de distribution des variables reponses en supposant que les donn ´ ees soient manquantes au hasard (MAR), sous un mod ´ ele ` de regression non param ´ etrique. Notre approche permet l’estimation coh ´ erente de n’importe quelle fonction- ´ nelle faiblement continue de la distribution des variables reponses. Plus particuli ´ erement, nous proposons des ` L- et M-fonctionnelles qui sont des estimateurs fortement coherents de n’importe quelle fonctionnelle con- ´ tinue du parametre de position (par exemple, la m ` ediane). Une m ´ ethode d’ajustement robuste du mod ´ ele de ` regression combin ´ ee aux propri ´ et´ es de robustesse des fonctionnelles de tendance centrale fournissent une ´ methode robuste pour l’estimation du param ´ etre de position. La robustesse de notre proc ` edure est mesur ´ ee´ a l’aide du point de rupture. Nous obtenons aussi la fonction de distribution asymptotique des estimateurs ` du parametre de position. Des suppl ` ements, contenant les d ´ emonstrations des th ´ eor ´ emes, sont disponibles ` en ligne. Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Continuum solvent models have become a standard technique in the context of electronic structure calculations, yet, no implementations have been reported capable to perform molecular dynamics at solid-liquid interfaces. We propose here such a continuum approach in a DFT framework, using plane-waves basis sets and periodic boundary conditions. Our work stems from a recent model designed for Car-Parrinello simulations of quantum solutes in a dielectric medium [J. Chem. Phys. 124, 74103 (2006)], for which the permittivity of the solvent is defined as a function of the electronic density of the solute. This strategy turns out to be inadequate for systems extended in two dimensions, by introducing new term in the Kohn-Sham potential which becomes unphysically large at the interfacial region, seriously affecting the convergence. If the dielectric medium is properly redefined as a function of the atomic coordinates, a good convergence is obtained and the constant of motion is conserved during the molecular dynamics simulations. Moreover, a significant gain in efficiency can be achieved if the simulation box is partitioned in two, solving the Poisson problem separately for the "dry" region using fast Fourier transforms, and for the solvated or "wet" region using a multigrid method. Eventually both solutions are combined in a self-consistent procedure, and in this way Car-Parrinello molecular dynamics simulations of solid-liquid interfaces can be performed at a very moderate computational cost. This scheme is employed to investigate the acid-base equilibrium at the TiO2-water interface., Comment: 36 pages, 7 figures

We consider a random tree and introduce a metric in the space of trees to define the ``mean tree'' as the tree minimizing the average distance to the random tree. When the resulting metric space is compact we have laws of large numbers and central limit theorems for sequence of independent identically distributed random trees. As application we propose tests to check if two samples of random trees have the same law., REvised version, 15 pages. An error on the parameter-range is corrected. Now z

We consider a Bernoulli oriented percolation model on the subgraph of ℤ2 consisting of the points whose coordinates add up to an even number. In this model, each point (m, n) of this graph is the origin of two oriented bonds whose end points are (m+1, n+1) and (m-1, n+1). We show that the probability that there is an open path from (0,0) to (2i, 2n) decreases strictly as i increases from 0 to n.

Comment on ``Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable'' [arXiv:0804.2958], Comment: Published in at http://dx.doi.org/10.1214/07-STS227D the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The mechanisms involved in bidirectional transport along microtubules remain largely unknown. We explored the collective action of kinesin-2 and dynein motors during transport of melanosomes in Xenopus laevis melanophores. These motors are attached to organelles through accessory proteins establishing a complex molecular linker. We determined both the stiffness of this linker and the organelles speed and observed that these parameters depended on the organelle size and cargo direction. Our results suggest that melanosome transport is driven by two dissimilar teams: whereas dynein motors compete with kinesin-2 affecting the properties of plus-end directed organelles, kinesin-2 does not seem to play a similar role during minus-end transport. Fil: de Rossi, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: de Rossi, Maria Emilia. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Sued, Raquel Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina Fil: Bruno, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Levi, Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina

In many practical problems related to supervised statistical learning, we are interested in predicting a continuous target. Frequently, the relationship between the explanatory variable and the target variable is nonlinear, so models that introduce nonlinearity for this purpose tend to obtain better performances in general. A statistical model that addresses this problem called the regression splines model has received considerable attention in recent years. This is due to its great predictive power and good fits incorporated by its flexibility. However, the splines regression model has a significant disadvantage: one of its main components, called knots, related to the change points, are usually chosen before the estimation process. They are considered pre-specified values, which in some situations can present severe problems in practical problems. In this work, we propose a new methodology that tries to solve this considering the knots location and knots number as parameters, and we solve this problem as an optimization approach using the nonlinear optimization algorithm BFGS. Furthermore, we introduce new regularization methods to penalize variables with irrelevant knots and avoid overfitting. The proposed methodology obtained many advantages compared to the approach used in the literature, such as automatic estimation of the number and location of knots, regularization methods that avoids overfitting, and selection of irrelevant knots. Our ap- proach obtained several gains in predictive performance and knots estimation in the simulations, thus obtaining better results than the usual procedure. Em muitos problemas práticos relacionados ao aprendizado estatístico supervisionado, estamos interessados em prever um alvo contínuo. Frequentemente, a relação entre a variável explicativa e o variável alvo é não linear, então os modelos que introduzem não linearidade para este propósito tendem a obter melhores performances em geral. Um modelo estatístico que aborda esse problema é chamado de regressão splines e tem recebido atenção considerável nos últimos anos. Isso se deve ao seu grande poder preditivo e bons ajustes incorporados pela sua flexibilidade. No entanto, o modelo de regressão splines tem um desvantagem significativa: um de seus principais componentes, chamados nós, relacionados aos pontos de mudança, geralmente são escolhidos antes do processo de estimação. São considerados valores pré-especificados, que em algumas situações podem apresentar problemas graves na prática. Neste trabalho, propomos uma nova metodologia que tenta resolver isso considerando a localização dos nós e o número de nós como parâmetros, e resolvemos esse problema como uma abordagem de otimização usando o algoritmo de otimização não linear BFGS. Além disso, introduzimos novos métodos de regularização para penalizar variáveis com nós e evitar overfitting. A metodologia proposta obteve muitas vantagens em relação à a abordagem utilizada na literatura, como a estimativa automática do número e localização de nós, métodos de regularização que evitam overfitting e seleção de nós irrelevantes. Nosso método obteve vários ganhos em desempenho preditivo e estimativa de nós nas simulações, obtendo assim melhores resultados do que o procedimento usual.