H1 Diffractive Structure Functions Measurement from new data

New measurements of the reduced cross section $\sigma_r^{D(3)}$ for the diffractive process $ep \to eXY$ in the kinematic domain $12 \leq Q^2 \leq 90$ GeV$^2$, $0.01 \leq \beta \leq 0.65$ and \xpom<0.1 are presented. Data events recorded by the H1 detector during the years 1999--2000 and 2004 have been used, corresponding to a total integrated luminosity of 68 pb$^{-1}$. The measurements are derived in the same range as previous H1 data, namely $M_Y < 1.6$ GeV and $|t| < 1.0$ GeV$^2$. Two different analysis methods, rapidity gap and $M_X$, are used and similar results are obtained in the kinematic domain of overlap. Finally, together with previous data, the diffractive structure function measurements are analysed with a model based on the dipole formulation of diffractive scattering. It is found to give a very good description of the data over the whole kinematic range.Comment: 4 pages, 4 figure; To appear in the proceedings of 14th International Workshop on Deep Inelastic Scattering (DIS 2006), Tsukuba, Japan, 20-24 Apr 200

Measurement Errors in Recall Food Expenditure Data

Household expenditure data is an important input into the study of consumption and savings behaviour and of living standards and inequality. Because it is collected in many surveys, food expenditure data has formed the basis of much work in these areas. Recently, there has been considerable interest in properties of different ways of collecting expenditure information. It has also been suggested that measurement error in expenditure data seriously affects empirical work based on such data. The Canadian Food Expenditure Survey asks respondents to first estimate their household's food expenditures and then record food expenditures in a diary for two weeks. This unique experiment allows us to compare recall and diary based expenditure data collected from the same individuals. Under the assumption that the diary measures are "true" food consumption, this allows us to observe errors in measures of recall food consumption directly, and to study the properties of those errors. Under this assumption, measurement errors in recall food consumption data appear to be substantial, and they do not have many of the properties of classical measurement error. In particular, they are neither uncorrelated with true consumption nor conditionally homoscedastic. In addition, they are not well approximated by either a normal or log normal distribution. We also show evidence that diary measures are themselves imperfect, suffering for example, from "diary exhaustion". This suggests alternative interpretations for the differences between recall and diary consumption measures. Finally, we compare estimates of income and household size elasticities of per capita food consumption based on the two kinds of expenditure data and, in contrast to some previous work, find little difference between the two.expenditure, consumption, surveys

THE MEASUREMENT OF THE ECONOMIC DISTANCE ON THE BASIS OF SYMBOLIC DATA

The economic distance defines a dissimilarity level between objects functioning in the economic space. It is one of the most important issues of spatial econometrics. However its measurement is difficult due to the definition, description and estimation problems. The objective of the paper is to indicate the role of symbolic data in describing the economic distance and also the way of its measurement using symbolic data analysis methods. A significance of the economic distance, measurement problems, symbolic data concept and dissimilarity measures, and also an empirical example were presented in the paper

Measurement set selection of parameter estimation in biological system modelling - a case study of signal transduction pathways

Parameter estimation is a challenging problem for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy and the cost of experiments high. Accurate recovery of parameters depends on the quality and quantity of measurement data. It is therefore important to know which measurements to be taken when and how through optimal experimental design (OED). In this paper a method was proposed to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory was used to examine the number of necessary measurement variables. The priority of each measurement variable was determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method were shown through an example of signal pathway model

Data for Democracy: Improving Elections Through Metrics and Measurement

Compiles essays on improving election data collection and reporting, management, and usage; how data improve elections; and other issues raised in a May 2008 conference; with policy recommendations. Includes a state-by-state assessment of data reporting

Sparse Identification of Nonlinear Duffing Oscillator From Measurement Data

In this paper we aim to apply an adaptation of the recently developed technique of sparse identification of nonlinear dynamical systems on a Duffing experimental setup with cubic feedback of the output. The Duffing oscillator described by nonlinear differential equation which demonstrates chaotic behavior and bifurcations, has received considerable attention in recent years as it arises in many real-world engineering applications. Therefore its identification is of interest for numerous practical problems. To adopt the existing identification method to this application, the optimization process which identifies the most important terms of the model has been modified. In addition, the impact of changing the amount of regularization parameter on the mean square error of the fit has been studied. Selection of the true model is done via balancing complexity and accuracy using Pareto front analysis. This study provides considerable insight into the employment of sparse identification method on the real-world setups and the results show that the developed algorithm is capable of finding the true nonlinear model of the considered application including a nonlinear friction term.Comment: 6 pages, 8 figures, conference pape