Smoothing: Local Regression Techniques

Smoothing methods attempt to find functional relationships between different measurements. As in the standard regression setting, the data is assumed to consist of measurements of a response variable, and one or more predictor variables. Standard regression techniques (Chapter ??) specify a functional form (such as a straight line) to describe the relation between the predictor and response variables. Smoothing methods take a more flexible approach, allowing the data points themselves to determine the form of the fitted curve. This article begins by describing several different approaches to smoothing, including kernel methods, local regression, spline methods and orthogonal series. A general theory of linear smoothing is presented, which allows us to develop methods for statistical inference, model diagnostics and choice of smoothing parameters. The theory is then extended to more general settings, including multivariate smoothing and likelihood models. --

On large-sample estimation and testing via quadratic inference functions for correlated data

Hansen (1982) proposed a class of "generalized method of moments" (GMMs) for estimating a vector of regression parameters from a set of score functions. Hansen established that, under certain regularity conditions, the estimator based on the GMMs is consistent, asymptotically normal and asymptotically efficient. In the generalized estimating equation framework, extending the principle of the GMMs to implicitly estimate the underlying correlation structure leads to a "quadratic inference function" (QIF) for the analysis of correlated data. The main objectives of this research are to (1) formulate an appropriate estimated covariance matrix for the set of extended score functions defining the inference functions; (2) develop a unified large-sample theoretical framework for the QIF; (3) derive a generalization of the QIF test statistic for a general linear hypothesis problem involving correlated data while establishing the asymptotic distribution of the test statistic under the null and local alternative hypotheses; (4) propose an iteratively reweighted generalized least squares algorithm for inference in the QIF framework; and (5) investigate the effect of basis matrices, defining the set of extended score functions, on the size and power of the QIF test through Monte Carlo simulated experiments.Comment: 32 pages, 2 figure

Smoothing: Local Regression Techniques

Smoothing methods attempt to find functional relationships between different measurements. As in the standard regression setting, the data is assumed to consist of measurements of a response variable, and one or more predictor variables. Standard regression techniques (Chapter ??) specify a functional form (such as a straight line) to describe the relation between the predictor and response variables. Smoothing methods take a more flexible approach, allowing the data points themselves to determine the form of the fitted curve. This article begins by describing several different approaches to smoothing, including kernel methods, local regression, spline methods and orthogonal series. A general theory of linear smoothing is presented, which allows us to develop methods for statistical inference, model diagnostics and choice of smoothing parameters. The theory is then extended to more general settings, including multivariate smoothing and likelihood models

A New Technique for Finding Needles in Haystacks: A Geometric Approach to Distinguishing Between a New Source and Random Fluctuations

We propose a new test statistic based on a score process for determining the statistical significance of a putative signal that may be a small perturbation to a noisy experimental background. We derive the reference distribution for this score test statistic; it has an elegant geometrical interpretation as well as broad applicability. We illustrate the technique in the context of a model problem from high-energy particle physics. Monte Carlo experimental results confirm that the score test results in a significantly improved rate of signal detection.Comment: 5 pages, 4 figure

Beyond 'Criminology vs. Zemiology': Reconciling crime with social harm

Since its emergence at the start of the twenty-first century, zemiology and the field of harm studies more generally, has borne an ambiguous and, at times, seemingly antipathetic relationship with the better-established field of criminology. Whilst the tension between the perspectives is, at times, overstated, attempts to reconcile the perspectives have also proved problematic, such that, at present, it appears that they risk either becoming polarized into mutually antagonistic projects, or harmonized to the point that zemiology is simply co-opted within criminology. Whilst tempting to view this as nothing more than an academic squabble, it is the central argument put forward in this chapter that the current trend towards either polariziaton or harmonization of the criminological and zemiological projects, risks impoverishing both perspectives, both intellectually and, more fundamentally, in terms of their capacity to effect meaningful social change. To this end, this chapter offers a critical reflection of recent attempts to reconcile the social harm perspective with criminology, focussing in particular on Majid Yar’s attempts to do so using the concept of ‘recognition’ derived from critical theory. It is suggested that such attempts, whilst important in the contribution they make to developing a theory of harm, are necessarily flawed by their reliance on an implicit assumption of a shared conception of harm underpinning both the concept of ‘crime’ and ‘social harm’. By contrast, it is the central argument put forward in this chapter that zemiology and criminology are best understood as divergent normative projects which, whilst sharing many of the same goals with regards to the improvement of the criminal justice system and the tackling of social problems, differ primarily in the means by which they seek to achieve these. Therefore, rather than denying this debate through the collapsing of one perspective into the other, or polarizing them into hostiles camps, it is only by recognising the nature of this debate and fostering dialogue between the perspectives that we can achieve our shared goals and effect meaningful change

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Global Boundary Stratotype Section and Point (GSSP) for the Anthropocene Series: Where and how to look for potential candidates

International audienc