Minority Enrollments at Public Universities of Diverse Selectivity Levels under Different Admission Regimes: The Case of Texas

This study describes how minority enrollment probabilities respond to changes in admission policies from affirmative-action to merit-only programs and then to percentage plans when the demographic composition of the potential pool of applicants is also shifting. It takes advantage of admission policy changes that occurred in the state of Texas with the Hopwood and HB588 decisions and of a unique administrative dataset that includes applications, admissions, and enrollments for three public universities of different selectivity levels. The findings suggest that the elimination of affirmative action and the introduction of the Top 10% plan had differential effects on minority enrollment probabilities as well as on application behavior depending on the selectivity level of the postsecondary institution. In particular, Hopwood is related to shifts in minority enrollments from selective institutions to less selective ones as the cascading hypothesis predicts. And although the Top 10% plan seems to have helped increased minority enrollment probabilities at the selective college as the upgrading hypothesis predicts, once the increases in minority shares among high-school graduates are taken into account, we find that the Top 10% plan can no longer be related to improvements in minority representation at selective universities.

Generating time series reference models based on event analysis

Creating a reference model that represents a given set of time series is a relevant problem as it can be applied to a wide range of tasks like diagnosis, decision support, fraud detection, etc. In some domains, like seismography or medicine, the relevant information contained in the time series is concentrated in short periods of time called events. In this paper, we propose a technique for generating time series reference models based on the analysis of the events they contain. The proposed technique has been applied to time series from two medical domains: Electroencephalography, a neurological procedure to record the electrical activity produced by the brain and Stabilometry, a branch of medicine studying balance-related functions in human beings

State-Uncertainty preferences and the Risk Premium in the Exchange rate market

This paper introduces state-uncertainty preferences into the Lucas (1982) economy, showing that this type of preferences helps to explain the exchange rate risk premium. Under these preferences we can distinguish between two factors driving the exchange rate risk premium: “macroeconomic risk” and “the risk associated with variation in the private agents’ perception on the level of uncertainty”. State-uncertainty preferences amount to assuming that a given level of consumption will yield a higher level of utility the lower is the level of uncertainty perceived by consumers. Furthermore, empirical evidence from three main European economies in the transition period to the euro provides empirical support for the modelRisk premium, taste shocks, fundamental uncertainty.

State-Uncertainty preferences and the Risk Premium in the Exchange rate market

This paper introduces state-uncertainty preferences into the Lucas (1982) economy, showing that this type of preferences helps to explain the exchange rate risk premium. Under these preferences we can distinguish between two factors driving the exchange rate risk premium: “macroeconomic risk” and “the risk associated with variation in the private agents’ perception on the level of uncertainty”. State-uncertainty preferences amount to assuming that a given level of consumption will yield a higher level of utility the lower is the level of uncertainty perceived by consumers. Furthermore, empirical evidence from three main European economies in the transition period to the euro provides empirical support for the modelForecasting, subspace methods, combining forecasts.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals