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Equality of opportunity is about leveling the playing field so that circumstances such as gender, ethnicity, place of birth, or family background do not influence a person's life chances. Success in life should depend on people's choices, effort and talents, not to their circumstances at birth. 'Measuring Inequality of Opportunities in Latin America and the Caribbean' introduces new methods for measuring inequality of opportunities and makes an assessment of its evolution in Latin America over a decade. An innovative Human Opportunity Index and other parametric and non-parametric techniques are presented for quantifying inequality based on circumstances exogenous to individual efforts. These methods are applied to gauge inequality of opportunities in access to basic services for children, learning achievement for youth, and income and consumption for adults.

Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade the spectrum utilization. In this paper, an algorithm for the detection of primary user emulation and jamming attacks in cognitive radio is proposed. The proposed algorithm is based on the sparse coding of the compressed received signal over a channel-dependent dictionary. More specifically, the convergence patterns in sparse coding according to such a dictionary are used to distinguish between a spectrum hole, a legitimate primary user, and an emulator or a jammer. The process of decision-making is carried out as a machine learning-based classification operation. Extensive numerical experiments show the effectiveness of the proposed algorithm in detecting the aforementioned attacks with high success rates. This is validated in terms of the confusion matrix quality metric. Besides, the proposed algorithm is shown to be superior to energy detection-based machine learning techniques in terms of receiver operating characteristics curves and the areas under these curves.

Partial residual plots have a long history and, judging from their prominence in the literature, are frequently used. In this article, I explore the structure and usefulness of partial residual plots and augmented partial residual plots as basic tools for dealing with curvature as a function of selected covariates x 2 in regression problems in which the covariate vector x is partitioned as x T = (x 1 T , x 2 T ). The usefulness of these plots for obtaining a good impression of curvature can depend on the behavior of the covariates through the conditional expectation E(x 1 |x 2 ). Partial residual plots seem to perform best under linear conditional expectations. Augmented partial residual plots allow E(x 1 |x 2 ) to be a quadratic function of x 2 . This development leads to a new class of plots, called CERES plots, that includes partial and augmented partial residual plots as special cases. CERES plots may be useful for obtaining an impression of curvature as a function of x 2 when the conditional expectations E(x 1 |x 2 ) are neither linear nor quadratic. The relationship between these developments and generalized additive models is discussed as well.

For a spatial point process model in which the intensity depends on spatial covariates, we develop graphical diagnostics for validating the covariate effect term in the model, and for assessing whether another covariate should be added to the model. The diagnostics are point-process counterparts of the well-known partial residual plots (component-plus-residual plots) and added variable plots for generalized linear models. The new diagnostics can be derived as limits of these classical techniques under increasingly fine discretization, which leads to efficient numerical approximations. The diagnostics can also be recognized as integrals of the point process residuals, enabling us to prove asymptotic results. The diagnostics perform correctly in a simulation experiment. We demonstrate their utility in an application to geological exploration, in which a point pattern of gold deposits is modeled as a point process with intensity depending on the distance to the nearest geological fault. Online supplementary materials include technical proofs, computer code, and results of a simulation study.

The improvement of anaerobic digestion was investigated in an interdisciplinary research group. Using four different methods of mechanical cell disintegration the influence of the degree of disintegration and the digestion parameters on the performance of the anaerobic process was investigated. Analytical methods to describe the degree of cell-disruption had to be developed. The best results were obtained using a stirred ball mill and a high-pressure homogenizer. As a result of disintegration the degradation is accelerated and the digestion time can be reduced, especially when using immobilized micro-organisms. The treatment of digested sludge by ozonization respectively by mechanical disintegration led to an improved biodegradability of residual organic compounds. In a following second anaerobic process the treated sludge reached an even higher degree of degradation. On the other hand the disruption of the particle structure leads to an increase in polymer-demand and no improvement in dewatering results. Sludge water, returned to the aeration tanks, is slightly more polluted, especially the concentration of ammonia increases because of the better anaerobic digestion.

Many of the existing measures for influential subsets in univariate ordinary least squares (OLS) regression analysis have natural extensions to the multivariate regression setting. Such measures may be characterized by functions of the submatrices H I of the hat matrix H, where I is an index set of deleted cases, and Q I , the submatrix of Q = E(E T E) −1 E T , where E is the matrix of ordinary residuals. Two classes of measures are considered: f(·)tr[H I Q I (I − H I − Q I ) a (I − H I ) b ] and f(·)det[(I − H I − Q I ) a (I − H I ) b ], where f is a scalar function of the dimensions of matrices and a and b are integers. These characterizations motivate us to consider separable leverage and residual components for multiple-case influence and are shown to have advantages in computing influence measures for subsets. In the recent statistical literature on regression analysis, much attention has been given to problems of detecting observations that, individually or jointly, exert a disproportionate influence on the outcome of univariate linear regression analysis and to assessing the influence of such cases, individually or jointly. By far the most popular approach is that of measuring the change in some feature of the analysis upon deletion of one or more of the cases. Various measures have been proposed that emphasize different aspects of influence on the regression. For a review of such methods, see Cook (1977, 1979), Belsley, Kuh, and Welsch (1980), Cook and Weisberg (1982), and Chatterjee and Hadi (1986, 1988). In this article we generalize some of the univariate measures of influence to the multivariate regression setting and then show that the generalized measures are special cases of two general classes of influence measures. There are other approaches to influence measures in regression diagnostics (see, for example, Cook 1986) that are not special cases of our general classes. The majority of the existing measures, however, are.

Added-variable plots are useful for a variety of data-analytic purposes but can be seriously misleading when used to investigate curvature and predictor transformations in linear regression, unless the predictors are independent.

This chapter contains sections titled: Introduction A Comprehensive Theory of Acoustic Echo Control The Kalman Filter for Conditional Mean and Covariance Estimation AEC Performance of the Frequency‐Domain Adaptive Kalman Filter Discussion and Conclusions Bibliography

This article concerns three-dimensional (3-D) added variable and partial residual plots. We show that the two-dimensional (2-D) added variable and partial residual plots are included as views in the 3-D plots, and these views are along (parallel to) the regression plane. That is, the 2-D plots are limited views, restricted to a sort of flatland, so they have a limited ability to show what is going on. In the case of just two predictors, recent improvements in the 2-D partial residual plot, such as the Mallows augmented plot, are also included as views in the 3-D plot, but these views are not along the regression plane.

This article discusses three well-known methods for obtaining a graphical evaluation of the effect of adding an explanatory variable in linear regression. A new method is also proposed.