Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of "significant places", thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.Comment: 11 pages, 8 figure

INFORMATION CRITERIA FOR IMPULSE RESPONSE FUNCTION MATCHING ESTIMATION OF DSGE MODELS

We propose new information criteria for impulse response function matching estimators (IRFMEs). These estimators yield sampling distributions of the structural parameters of dynamic sto- chastic general equilibrium (DSGE) models by minimizing the distance between sample and theoretical impulse responses. First, we propose an information criterion to select only the responses that produce consistent estimates of the true but unknown structural parameters: the Valid Impulse Response Selection Criterion (VIRSC). The criterion is especially useful for mis-speci?ed models. Second, we propose a criterion to select the impulse responses that are most informative about DSGE model parameters: the Relevant Im- pulse Response Selection Criterion (RIRSC). These criteria can be used in combination to select the subset of valid impulse response functions with minimal dimension that yields asymptotically e¢ cient estimators. The criteria are general enough to apply to impulse responses estimated by VARs, local projections, and simulation methods. We show that the use of our criteria signi?cantly a§ects estimates and inference about key parameters of two well-known new Keynesian DSGE models. Monte Carlo evidence indicates that the criteria yield gains in terms of ?nite sample bias as well as o§ering tests statistics whose behavior is better approximated by ?rst order asymptotic theory. Thus, our criteria improve on existing methods used to implement IRFMEs.Output Growth Forecasts, Inflation Forecasts, Model Selection, Structural Change, Forecast Evaluation, Real-time data. Evaluation

Knowledge, identities and dilemmas of the self in physical education teacher education

The purpose of this study was to understand how becoming a physical education teacher is shaped by personally and socially constructed knowledge and is affected by the rules and resources of the structural systems in which physical education teacher education (PETE) takes place. The study was influenced by the traditions of Personal Construct Theory (Kelly 1955), the theoretical tenets of social constructionism (Gergen 1991), and Giddens’s work on structuration (1984) and self-identity (1991). Ten PETE students participated in the study over almost three years. They undertook repertory grid sessions periodically through their study, followed by ‘learning conversations’, in which the grid itself was discussed, reworked and collaboratively analysed. All conversations were audio taped and were fully transcribed. The data were analysed in three ways, all of which were used to construct a story of the study. First, the grids were analysed for patterns, consistencies across students and for consistencies within students. These grids provided the first level story that related to constructions of knowledge. These constructions were then content analysed using analysis categories developed from Gergen’s notion of the saturated self and Giddens’ ideas of identity in late modernity. These analyses represented what Giddens calls a double hermeneutic since to all intents and purposes, the story of the study was constructed from the participants’ constructions of what it is to be a physical education teacher. The data suggests that during the process of constructing professional knowledge the student experienced a series of dilemmas of professional self-identity. It seems that to become a PE teacher, the dilemmas must be worked through until a position of what Giddens calls ontologist security has been achieved. Some students in this study had not managed to reach such a point before they left university and entered the teaching profession. In spite of this, the methods of the study allowed the participants to begin to articulate their theories and visions of teaching physical education. The therapeutic qualities of Kelly’s theory encouraged a number of the students to ‘see it differently’ (Rossi, 1997) and to begin to develop a rationale for physical education based on educational practice that considers the needs of individuals and the promotion of a socially just community. I have argued however that this ‘critical’ approach to physical education pedagogy was considered risky and as such students who were prepared to engage in such risk strategies also had other strategic relational selves (Gergen, 1991) to minimise risk at key times during their teacher education

Information criteria for impulse response function matching estimation of DSGE models

We propose a new information criterion for impulse response function matching estimators of the structural parameters of macroeconomic models. The main advantage of our procedure is that it allows the researcher to select the impulse responses that are most informative about the deep parameters, therefore reducing the bias and improving the efficiency of the estimates of the model’s parameters. We show that our method substantially changes key parameter estimates of representative dynamic stochastic general equilibrium models, thus reconciling their empirical results with the existing literature. Our criterion is general enough to apply to impulse responses estimated by vector autoregressions, local projections, and simulation methods.

Comparing the impacts of biofuels using survey and non-survey data

Title from PDF of title page (University of Missouri--Columbia, viewed on April 1, 2011).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Judith I. Stallmann.M.S. University of Missouri--Columbia 2010.This paper utilizes survey data to compare several non-survey methods of modeling the economic impacts of biofuels plants. It examines differences in the input coefficients derived from the survey versus the trade coefficients generated through the non-survey methods. It finds that of the three non-survey methods examined, the Swenson (2006) model input coefficients most closely represent those found in the survey based on the performance of the non-survey model input coefficients in a variety of statistical tests. Further, it examines the economic impacts (multipliers) generated by these models compared to those generated from the survey. Based upon statistical tests of the multipliers, the Swenson model's estimated impacts most closely represent the impacts derived from the survey.Includes bibliographical reference

Representation of Functional Data in Neural Networks

Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data analysis.Comment: Also available online from: http://www.sciencedirect.com/science/journal/0925231

Support vector machine for functional data classification

In many applications, input data are sampled functions taking their values in infinite dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate modifications of them. In fact most of the traditional data analysis tools for regression, classification and clustering have been adapted to functional inputs under the general name of functional Data Analysis (FDA). In this paper, we investigate the use of Support Vector Machines (SVMs) for functional data analysis and we focus on the problem of curves discrimination. SVMs are large margin classifier tools based on implicit non linear mappings of the considered data into high dimensional spaces thanks to kernels. We show how to define simple kernels that take into account the unctional nature of the data and lead to consistent classification. Experiments conducted on real world data emphasize the benefit of taking into account some functional aspects of the problems.Comment: 13 page

Size-based scheduling vs fairness for datacenter flows: a queuing perspective

Contrary to the conclusions of a recent body of work where approximate shortest remaining processing time first (SRPT) flow scheduling is advocated for datacenter networks, this paper aims to demonstrate that per-flow fairness remains a preferable objective. We evaluate abstract queuing models by analysis and simulation to illustrate the non-optimality of SRPT under the reasonable assumptions that datacenter flows occur in batches and bursts and not, as usually assumed, individually at the instants of a Poisson process. Results for these models have significant implications for the design of bandwidth sharing strategies for datacenter networks. In particular, we propose a novel "virtual fair scheduling" algorithm that enforces fairness between batches and is arguably simple enough to be implemented in high speed devices.Comment: 16 pages, 5 figure

Third class county budget trend analysis : 1996-2013 workbook user's manual

The electronic workbook, Third Class County Budget Trend Analysis: 1996-2013, is a set of spreadsheets, or workbook, that enables Missouri third-class counties to analyze county fiscal trends from 1996 to 2013