Commodities and cognition

A commentary on "An enquiry concerning the nature of conceptual categories: a case-study on the social dimension of human cognition", by John Stewart (2014)in 'Frontiers in Psychology', Vol.5

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

From Near to Far: Maria Short and the Places and Spaces of Science in Edinburgh from 1736 to 1850

A relatively unknown woman named Maria Theresa Short opened a popular observatory in 1835 in Ed inburgh - a time and place where men of science and property had long failed to make a viable space for astronomy. She exhibited scientific instruments to a general public, along with a great telescope and a walk-in camera obscura that projected live views of the city and continues to delight audiences to this day. To better understand Short's accomplishments, achieved as scientific and public life became increasingly closed to women, this study explores her largely untold story, and maps some of the places of science around it. Finding local contingencies, multiple sites and practices by diverse groups, it proposes that tensions within the connections between science and spectacle and the use of popularization to promote its professionalization produced gaps that even a marginal figure like Maria Short could inhabit and exploit

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. --

Top-Quark Mass Measurement in the Dilepton Channel Using {\it in situ} Jet Energy Scale Calibration

We employ a top-quark mass measurement technique in the dilepton channel with {\it in situ} jet energy scale calibration. Three variables having different jet energy scale dependences are used simultaneously to extract not only the top-quark mass but also the energy scale of the jet from a single likelihood fit. Monte Carlo studies with events corresponding to an integrated luminosity of 5 fb$^{-1}$ proton-proton collisions at the Large Hadron Collider $\sqrt{s} = 7$ TeV are performed. Our analysis suggests that the overall jet energy scale uncertainty can be significantly reduced and the top-quark mass can be determined with a precision of less than 1 GeV/c$^2$, including jet energy scale uncertainty, at the Large Hadron Collider.Comment: Submitted to Phys. Rev.

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