A subsampling approach to estimating the distribution of diversing statistics with application to assessing financial market risks

In this paper we propose a subsampling estimator for the distribution ofstatistics diverging at either known rates when the underlying timeseries in strictly stationary abd strong mixing. Based on our results weprovide a detailed discussion how to estimate extreme order statisticswith dependent data and present two applications to assessing financialmarket risk. Our method performs well in estimating Value at Risk andprovides a superior alternative to Hill's estimator in operationalizingSafety First portofolio selection.

Exact and approximate stepdown methods for multiple hypothesis testing

Consider the problem of testing k hypotheses simultaneously. In this paper,we discuss finite and large sample theory of stepdown methods that providecontrol of the familywise error rate (FWE). In order to improve upon theBonferroni method or Holm's (1979) stepdown method, Westfall and Young(1993) make eective use of resampling to construct stepdown methods thatimplicitly estimate the dependence structure of the test statistics. However,their methods depend on an assumption called subset pivotality. The goalof this paper is to construct general stepdown methods that do not requiresuch an assumption. In order to accomplish this, we take a close look atwhat makes stepdown procedures work, and a key component is a monotonicityrequirement of critical values. By imposing such monotonicity on estimatedcritical values (which is not an assumption on the model but an assumptionon the method), it is demonstrated that the problem of constructing a validmultiple test procedure which controls the FWE can be reduced to the problemof contructing a single test which controls the usual probability of a Type 1error. This reduction allows us to draw upon an enormous resamplingliterature as a general means of test contruction.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Contribución al diseño de GNSS-SDR, un receptor GNSS de código abierto

[ANGLÈS] This project introduces GNSS-SDR, an open source Global Navigation Satellite System software-defined receiver. The lack of reconfigurability of current commercial-of-the-shelf receivers and the advent of new radionavigation signals and systems make software receivers an appealing approach to design new architectures and signal processing algorithms. With the aim of exploring the full potential of this forthcoming scenario with a plurality of new signal structures and frequency bands available for positioning, this paper describes the software architecture design and provides details about its implementation, targeting a multiband, multisystem GNSS receiver. The result is a testbed for GNSS signal processing that allows any kind of customization, including interchangeability of signal sources, signal processing algorithms, interoperability with other systems, output formats, and the offering of interfaces to all the intermediate signals, parameters and variables. The source code release under the GNU General Public License (GPL) secures practical usability, inspection, and continuous improvement by the research community, allowing the discussion based on tangible code and the analysis of results obtained with real signals. The source code is complemented by a development ecosystem, consisting of a website (http://gnss-sdr.org), as well as a revision control system, instructions for users and developers, and communication tools. The project shows in detail the design of the initial blocks of the Signal Processing Plane of the receiver: signal conditioner, the acquisition block and the receiver channel, the project also extends the functionality of the acquisition and tracking modules of the GNSS-SDR receiver to track the new Galileo E1 signals available. Each section provides a theoretical analysis, implementation details of each block and subsequent testing to confirm the calculations with both synthetically generated signals and with real signals from satellites in space.