Time filter

Source Type

Buenos Aires, Argentina

The Torcuato Di Tella University is a non-profit private university founded in 1991. Located in the Belgrano neighborhood of Buenos Aires, Argentina, it has an undergraduate enrollment of 1,200 students and a graduate enrollment of 1,300. The university is focused primarily on social science. The undergraduates majors available are economics, business economics, business administration, law, political science, international relations, social science, history and architecture. The university also offers over 30 graduate programs.The faculty comprises 77 full-time professors, most of them graduate students and visiting scholars from universities abroad. The university provides more than 50 exchange programs with universities in Europe, North America, South America, Australia, Africa and Asia. There is also a sizable number of international students that study in the University for a semester or two. Although economic problems have delayed the expansion of the campus, the university is moving to a new building which will allow further growth in the number of students and majors. The university's dean is Ernesto Schargrodsky. Wikipedia.

Tchetgen Tchetgen E.J.,Harvard University | Robins J.M.,Harvard University | Rotnitzky A.,Torcuato Di Tella University
Biometrika | Year: 2010

We consider the doubly robust estimation of the parameters in a semiparametric conditional odds ratio model. Our estimators are consistent and asymptotically normal in a union model that assumes either of two variation independent baseline functions is correctly modelled but not necessarily both. Furthermore, when either outcome has finite support, our estimators are semiparametric efficient in the union model at the intersection submodel where both nuisance functions models are correct. For general outcomes, we obtain doubly robust estimators that are nearly efficient at the intersection submodel. Our methods are easy to implement as they do not require the use of the alternating conditional expectations algorithm of Chen (2007). 2009 Biometrika Trust.

Merener N.,Torcuato Di Tella University | Vicchi L.,Instituto Nacional Of Matematica Pura E Aplicada
Journal of Computational Finance | Year: 2015

We develop an efficient Monte Carlo method for the valuation of financial contracts on discretely realized variance. We work with a general stochastic volatility model that makes realized variance dependent on the full path of the asset price. The variance contract price is a high-dimensional integral over the fundamental sources of randomness. We identify a two-dimensional manifold that drives most of the uncertainty in realized variance, and we compute the contract price by combining precise integration over this manifold, implemented as fine stratification or deterministic sampling with quasirandom numbers, with conditional Monte Carlo on the remaining dimensions. For a subclass of models and a class of nonlinear payoffs, we derive approximate theoretical results that quantify the variance reduction achieved by our method. Numerical tests for the discretized versions of the widely used Hull–White and Heston models show that the algorithm performs significantly better than a standard Monte Carlo, even for fixed computational budgets. © 2015 Incisive Risk Information (IP) Limited.

Orellana L.,University of Buenos Aires | Rotnitzky A.,Torcuato Di Tella University | Robins J.M.,Harvard University
International Journal of Biostatistics | Year: 2010

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results. © 2010 The Berkeley Electronic Press. All rights reserved.

Reis S.D.S.,City College of New York | Reis S.D.S.,Federal University of Ceara | Hu Y.,City College of New York | Babino A.,FCEN UBA | And 6 more authors.
Nature Physics | Year: 2014

Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly. Theory has shown that connecting random networks may very easily result in abrupt failures. This finding reveals an intriguing paradox: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theory.

Tchetgen Tchetgen E.J.,Harvard University | Rotnitzky A.,Harvard University | Rotnitzky A.,Torcuato Di Tella University
Statistics in Medicine | Year: 2011

Modern epidemiologic studies often aim to evaluate the causal effect of a point exposure on the risk of a disease from cohort or case-control observational data. Because confounding bias is of serious concern in such non-experimental studies, investigators routinely adjust for a large number of potential confounders in a logistic regression analysis of the effect of exposure on disease outcome. Unfortunately, when confounders are not correctly modeled, standard logistic regression is likely biased in its estimate of the effect of exposure, potentially leading to erroneous conclusions. We partially resolve this serious limitation of standard logistic regression analysis with a new iterative approach that we call ProRetroSpective estimation, which carefully combines standard logistic regression with a logistic regression analysis in which exposure is the dependent variable and the outcome and confounders are the independent variables. As a result, we obtain a correct estimate of the exposure-outcome odds ratio, if either thestandard logistic regression of the outcome given exposure and confounding factors is correct, or the regression model of exposure given the outcome and confounding factors is correct but not necessarily both, that is, it is double-robust. In fact, it also has certain advantadgeous efficiency properties. The approach is general in that it applies to both cohort and case-control studies whether the design of the study is matched or unmatched on a subset of covariates. Finally, an application illustrates the methods using data from the National Cancer Institute's Black/White Cancer Survival Study. Copyright © 2010 John Wiley & Sons, Ltd.

Discover hidden collaborations