Rényi statistics for testing equality of autocorrelation coefficients

The problem of testing for equality of autocorrelation coefficients of two populations in multivariate data when errors are autocorrelated is considered. We derive Rényi statistics defined as divergences between unrestricted and restricted estimated joint probability density functions and we show that they are asymptotically chi-square distributed under the null hypothesis of interest. Monte Carlo simulation experiments are carried out to investigate the behavior of Rényi statistics and to make comparisons with test statistics based on the approach of Bhandary [M. Bhandary, Test for equality of autocorrelation coefficients for two populations in multivariate data when the errors are autocorrelated, Statistics & Probability Letters 73 (2005) 333–342] for the problem under consideration. Rényi statistics showed to have significantly better behavior.     

Race and the NFL Draft: Views from the Auction Block

Previous research suggests that minorities are more likely to perceive racially-based discrimination in a variety of settings than are whites, in large part because of the ways their personal experiences with racism shape the lens they use to view the world. We examine a labor market that is typically considered an exception to patterns of racism in employment, the industry of professional football. We interview athletes who attempted to gain employment in the National Football League, a labor market where access to valued positions is heavily restricted by industry practices. Findings from field research and semi-structured interviews indicate that minority workers experience symbolic discrimination during the hiring process. Differential treatment of players reflects stereotypes about minority families and masculinity. Although minority and white players describe much of the actual content of their labor market experiences in similar fashion, their perceptions of these experiences differ sharply, with minority athletes identifying far more negative repercussions.

Extensions of empirical likelihood and chi-squared-based tests for ordered alternatives

Several methods for comparing k populations have been proposed in the literature. These methods assess the same null hypothesis of equal distributions but differ in the alternative hypothesis they consider. We focus on two important alternative hypotheses: monotone and umbrella ordering. Two new families of test statistics are proposed, including two known tests, as well as two new powerful tests under monotone ordering. Furthermore, these families are adapted for testing umbrella ordering. We compare some members of the families with respect to power and Type I errors under different simulation scenarios. Finally, the methods are illustrated in several applications to real data.     

The correlated random walk in continuous time with barriers

In this paper correlation has been introduced between two successive transitions in the model investigated by Moyal and Heathcote (1959). The transition rate has been taken to be constant and for simplicity of mathematical expressions the time scale has been so chosen that this rate is unity. For the unrestricted walk we find that the mean position of the particle at time t is that position for which the probability of the particle arriving at some earlier instant from the left is 1/2. With both the barriers either absorbing or reflecting, we have obtained the Laplace Transform of the distributions, but because of the complicated nature of the mathematical expressions involved we are able to find only the asymptotic distributions. For the reflecting barrier case, we find that the process tends to a stationary distribution and that the expected position is eventually the middle point between the barriers.     

Gerrymandering on Georgia's Mind: The Effects of Redistricting on Vote Choice in the 2006 Midterm Election*

Objective. We make use of individual‐level survey data from the 2006 midterm election in order to determine the degree to which redistricting affected the vote choice of whites residing in Georgia Congressional Districts 8 and 12. Methods. A multivariate probit model was used to assess the probability of voting for the GOP House candidate among voters represented by the same incumbent before and after redistricting versus voters who had been newly drawn into one of these districts. Results. Despite a national tide that favored the Democratic Party in the 2006 elections, redrawn whites were more likely to vote for the Republican challengers in the districts surveyed. Conclusions. Our findings indicate that redistricting can be used to dampen the incumbency advantage. In addition, the findings of this research also speak to the continuing Republican realignment of white voters in the Deep South and to the recognition that the effects of redistricting are dependent on political context.     

Residual analysis and outliers in loglinear models based on phi-divergence statistics

In this paper we consider new families of residuals and influential measures, under the assumption of multinomial sampling, for loglinear models. These new families are based on φ$\phi$-divergence test statistic. The asymptotic normality of the standardized residuals is obtained as well as the relation of the new family of influential measures with the appropriate Cook's distance in this context. The expression of the new family of residuals is obtained in two important problems: independence and symmetry in two-dimensional contingence tables. A numerical example illustrates the results obtained.     

Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models

In this article, we discuss an outage‐forecasting model that we have developed. This model uses very few input variables to estimate hurricane‐induced outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.     

Climate,agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques

An estimated 1 billion people suffer from hunger worldwide, and climate change, urbanization, and globalization have the potential to exacerbate this situation. Improved models for predicting food security are needed to understand these impacts and design interventions. However, food insecurity is the result of complex interactions between physical and socio-economic factors that can overwhelm linear regression models. More sophisticated data-mining approaches could provide an effective way to model these relationships and accurately predict food insecure situations. In this paper, we compare multiple regression and data-mining methods in their ability to predict the percent of a country's population that suffers from undernourishment using widely available predictor variables related to socio-economic settings, agricultural production and trade, and climate conditions. Averaging predictions from multiple models results in the lowest predictive error and provides an accurate method to predict undernourishment levels. Partial dependence plots are used to evaluate covariate influence and demonstrate the relationship between food insecurity and climatic and socio-economic variables. By providing insights into these relationships and a mechanism for predicting undernourishment using readily available data, statistical models like those developed here could be a useful tool for those tasked with understanding and addressing food insecurity.