Parameterization and inference for nonparametric regression problems

We consider local likelihood or local estimating equations, in which a multivariate function () is estimated but a derived function () of () is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of (). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function () is facilitated without a loss of power by reparameterization so that () is itself a component of (). Our approach is in contrast with the standard practice of choosing () for convenience and allowing ()> to be a non-linear function of (). The methods are applied to an important data set in nutritional epidemiology.     

Capital,conflict and convergence: a political understanding of neoliberalism and its relationship to capitalist transformation

ABSTRACT

In this article we argue against influential analyses of neoliberalism that prioritize variegation and the role of ideas as key theoretical foci relevant to understanding neoliberalism’s diffusion into myriad national and political settings. Rather, we contend that crucial to understanding neoliberalism is the role of politically-produced convergence around market rationality that reflects two core processes: the reorganization of production and the ascendency of financialization. We present a theorization and analysis of neoliberalism’s political production and diffusion over time, explaining its contested evolution and impact across diverse settings (both ‘North’ and ‘South’) and emphasizing its ever-intensifying symbiotic relationship with the consolidating world market in which the former has increasingly come to serve as the latter’s operating system (OS). Further, we posit that neoliberalism’s form, function and impact demand analytically prioritizing the leverage of constellations of ideological and material interests within the contradictory context of consolidating relations of production and financialization. Our analysis thus challenges many previous expositions of neoliberalism for their failure to locate neoliberalism’s manifestation as arising out of social conflict within particular junctures that privilege certain social forces and ideas over others. We also distinguish our position by highlighting how manifestations of neoliberalism in various settings have combined to yield a greater world market in which variegation has gradually given way to ever-intensifying disciplinary pressures towards market-policy conformity (mono-policy). While current populist movements may well turn out to be important counter movements to neoliberal hegemony, especially if they can internationalize, the disciplining effect of the world market renders many nationally-oriented policy alternatives costly and politically fraught.     

On the need for increased rigour and care in the conduct and interpretation of network meta‐analyses in drug development

The rise over recent years in the use of network meta‐analyses (NMAs) in clinical research and health economic analysis is little short of meteoric driven, in part, by a desire from decision makers to extend inferences beyond direct comparisons in controlled clinical trials. But is the increased use and reliance of NMAs justified? Do such analyses provide a reliable basis for the relative effectiveness assessment of medicines and, in turn, for critical decisions relating to healthcare access and provisioning? And can such analyses also be used earlier, as part of the evidence base for licensure? Despite several important publications highlighting inherently unverifiable assumptions underpinning NMAs, these assumptions and associated potential for serious bias are often overlooked in the reporting and interpretation of NMAs. A more cautious, and better informed, approach to the use and interpretation of NMAs in clinical research is warranted given the assumptions that sit behind such analyses. Copyright © 2015 John Wiley & Sons, Ltd.     

Spatially-adaptive Penalties for Spline Fitting

The paper studies spline fitting with a roughness penalty that adapts to spatial heterogeneity in the regression function. The estimates are p th degree piecewise polynomials with p − 1 continuous derivatives. A large and fixed number of knots is used and smoothing is achieved by putting a quadratic penalty on the jumps of the p th derivative at the knots. To be spatially adaptive, the logarithm of the penalty is itself a linear spline but with relatively few knots and with values at the knots chosen to minimize the generalized cross validation (GCV) criterion. This locally-adaptive spline estimator is compared with other spline estimators in the literature such as cubic smoothing splines and knot-selection techniques for least squares regression. Our estimator can be interpreted as an empirical Bayes estimate for a prior allowing spatial heterogeneity. In cases of spatially heterogeneous regression functions, empirical Bayes confidence intervals using this prior achieve better pointwise coverage probabilities than confidence intervals based on a global-penalty parameter. The method is developed first for univariate models and then extended to additive models.     

Variance estimation in the analysis of microarray data

Summary.  Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t -statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which has led many researchers to assume that the variance is a function of the mean. Here we concentrate on estimation of the variance as a function of an unknown mean in two models: the constant coefficient of variation model and the quadratic variance–mean model. Because the means are unknown and estimated with few degrees of freedom, naive methods that use the sample mean in place of the true mean are generally biased because of the errors-in-variables phenomenon. We propose three methods for overcoming this bias. The first two are variations on the theme of the so-called heteroscedastic simulation–extrapolation estimator, modified to estimate the variance function consistently. The third class of estimators is entirely different, being based on semiparametric information calculations. Simulations show the power of our methods and their lack of bias compared with the naive method that ignores the measurement error. The methodology is illustrated by using microarray data from leukaemia patients.     

Children's Anger Attribution Bias: Relations to Family Environment and Social Adjustment

We examined the relations of caregiver depression and family instability to preschool children's anger attribution bias and emotion attribution accuracy on a test of emotion situation knowledge. After controlling for age, gender, and verbal ability, caregiver depression and family instability predicted children's anger attribution bias but not the overall accuracy of their emotion attributions. We also divided children into groups low and high on teacher reports of aggression and groups low and high on teacher reports of peer rejection and examined the anger attribution bias of these groups. For boys but not girls, greater anger attribution bias predicted higher levels of aggression. For all children, greater anger attribution bias predicted higher levels of peer rejection. Results suggest that the misattribution of anger to others may be an important component of some children's early emotional and social difficulties.