Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using Subsampling

A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

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.

Subsampling the mean of heavy-tailed dependent observations

We establish the validity of subsampling confidence intervals for themean of a dependent series with heavy-tailed marginal distributions.Using point process theory, we study both linear and nonlinear GARCH-liketime series models. We propose a data-dependent method for the optimalblock size selection and investigate its performance by means of asimulation study.

Subsampling inference in threshold autoregressive models

This paper discusses inference in self exciting threshold autoregressive (SETAR)models. Of main interest is inference for the threshold parameter. It iswell-known that the asymptotics of the corresponding estimator depend uponwhether the SETAR model is continuous or not. In the continuous case, thelimiting distribution is normal and standard inference is possible. Inthe discontinuous case, the limiting distribution is non-normal and cannotbe estimated consistently. We show valid inference can be drawn by theuse of the subsampling method. Moreover, the method can even be extendedto situations where the (dis)continuity of the model is unknown. In thiscase, also the inference for the regression parameters of the modelbecomes difficult and subsampling can be used advantageously there aswell. In addition, we consider an hypothesis test for the continuity ofthe SETAR model. A simulation study examines small sample performance.

Within-plot subsampling of trees for assessment in progeny trials of Scots pine

Summary

Analysis of frame-compatible subsampling structures for efficient 3DTV broadcast

The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences.

Gluttonous predators: how to estimate prey size when there are too many prey

Prey size is an important factor in food consumption. In studies of feeding ecology, prey items are usually measured individually using calipers or ocular micrometers. Among amphibians and reptiles, there are species that feed on large numbers of small prey items (e.g. ants, termites). This high intake makes it difficult to estimate prey size consumed by these animals. We addressed this problem by developing and evaluating a procedure for subsampling the stomach contents of such predators in order to estimate prey size. Specifically, we developed a protocol based on a bootstrap procedure to obtain a subsample with a precision error of at the most 5%, with a confidence level of at least 95%. This guideline should reduce the sampling effort and facilitate future studies on the feeding habits of amphibians and reptiles, and also provide a means of obtaining precise estimates of prey size.

Assessing sample representativeness for inorganic chemical investigation of invertebrates by INAA

The quality of environmental studies depends on the utilization of adequate sampling protocol and analytical method for obtaining reliable results and minimizing analytical uncertainties. In order to demonstrate the applicability of INAA for determining chemical element composition of invertebrates, this work evaluated sample representativeness in terms of subsampling and sample size. Br, Co, Fe, K, Na, Sc and Zn could be determined in very small samples despite increasing of analytical uncertainties. Special attention should be directed to invertebrate species with small structures because of the high chemical variation observed among different sample sizes tested.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Long-term biosignals visualization and processing

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

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.

Inference for nonparametric high-frequency estimators with an application to time variation in betas

We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. We suggest a procedure for a data-driven choice of the bandwidth parameters. Our simulation study indicates that the subsampling method is much more robust than the plug-in method based on the asymptotic expression for the variance. Importantly, the subsampling method reliably estimates the variability of the Two Scale estimator even when its parameters are chosen to minimize the finite sample Mean Squared Error; in contrast, the plugin estimator substantially underestimates the sampling uncertainty. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semi-definite. We use the subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas within year 2006, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every five or twenty minutes. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe.

Measuring and testing for the systemically important financial institutions

This paper analyzes the measure of systemic importance ∆CoV aR proposed by Adrian and Brunnermeier (2009, 2010) within the context of a similar class of risk measures used in the risk management literature. In addition, we develop a series of testing procedures, based on ∆CoV aR, to identify and rank the systemically important institutions. We stress the importance of statistical testing in interpreting the measure of systemic importance. An empirical application illustrates the testing procedures, using equity data for three European banks.