First and Second Derivatives in Time Series Classification Using DTW

In our previous work, we developed a new distance function based on a derivative and showed that our algorithm is effective. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than standard distance of function (value) comparison. The new distance was used in classification with the nearest neighbor rule. Now we improve on our previous technique by adding the second derivative. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 time series datasets from a wide variety of application domains. Our experiments show that this new method provides a significantly more accurate classification on the examined datasets.     

Multivariate Control Charts Based on Hybrid Novelty Scores

We propose a new nonparametric multivariate control chart that integrates a novelty score. The proposed control chart uses as its monitoring statistic a hybrid novelty score, calculated based on the distance to local observations as well as on the distance to the convex hull constructed by its neighbors. The control limits of the proposed control chart were established based on a bootstrap method. A rigorous simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compare it with existing multivariate control charts in terms of average run length (ARL) performance. The simulation results showed that the proposed control chart outperformed both the parametric and nonparametric Hotelling's T ² control charts, especially in nonnormal situations. Moreover, experimental results with real semiconductor data demonstrated the applicability and effectiveness of the proposed control chart. To increase the capability to detect small mean shift, we propose an exponentially weighted hybrid novelty score control chart. Simulation results indicated that exponentially weighted hybrid score charts outperformed the hybrid novelty score based control charts.     

Computational schema on ridge analysis

The purpose of this paper is to revisit the response surface technique ridge analysis within the context of the “trust region” problem in numerical analysis. It is found that these two approaches inherently solve the same problem. We introduce the computational difficulty, termed the “hard case”, which originates in the trust region methods, also exists in ridge analysis but has never been formally discussed in response surface methodology (RSM). The dual response global optimization algorithm (DRSALG) based on the trust region method is applied (with a certain modification) to solving the ridge analysis problem. Some numerical comparisons against a general-purpose nonlinear optimization algorithm are illustrated in terms of examples appearing in the literature     

Graphical Display of Means

A graphical procedure for the display of treatment means that enables one to determine the statistical significance of the observed differences is presented. It is shown that the widely used least significant difference and honestly significant difference statistics can be used to construct plots in which any two means whose uncertainty intervals do not overlap are significantly different at the assigned probability level. It is argued that these plots, because of their straightforward decision rules, are more effective than those that show the observed means with standard errors or confidence limits. Several examples of the proposed displays are included to illustrate the procedure.     

Statistical Computing: History and Trends

The evolution of computers is currently in a period of rapid change, stimulated by radically cheaper and smaller devices for processing and memory. These changes are certain to provide major opportunities and challenges for the use of computers in statistics. This article looks at history and current trends, in both general computing and statistical computing, with the goal of identifying key features and requirements for the near future. A discussion of the S language developed at Bell Laboratories illustrates some program design principles that can make future work on statistical programs more effective and more valuable.     

Book reviews

Donald J. Koosis: Statistics. A Self-Teaching Guide, Fourth Edition. Wiley 1997, ISBN 0-471-14688-9

Christopher C. Heyde: Quasi-Likelihood and Its Applications, Springer Series in Statistics, 1997, pp. 235, [ISBN 0-387-98225-61

Jeffrey S. Simonoff: Smoothing Methods in Statistics, Springer Series in Statistics, 1996, pp. 338

Andr´e I. Khuri, Thomas Mathew and Birmal K. Sinha: Statistical Tests for Mixed Linear Models, Wiley Series in Probability and Statistics, 1998, pp. 352, PSBN 0-471-1 5653-11

Ronald Christensen: Log-Linear Models and Logistic Regression, Springer Texts in Statistics, 1997, pp. 483, [ISBN 0-387-98247-71

E. L. Lehmann: Testing Statistical Hypotheses, Springer Texts in Statistics, 1997, pp. 600, [ISBN 0-387-94919-41

S. R. Searle, G. Casella and Ch. E. McCulloch: Variance Components, Wiley and Sons, 1992, pp. 496, [ISBN 0-471-62162-51

B. J. T. Morgan: Analysis of Quantal Response Data, Chapman & Hall, 1992, pp. 51 1

Carl D. Huberty: Applied Discriminant Analysis, Wiley and Sons, 1994, pp. 490

C. R. Rao and H. Toutenburg: Linear Models. Least Squares and Alternatives, Springer Series in Statistics, 1995, 188 pp., [ISBN 0-387-94562-81     

INFLUENCE FUNCTIONS IN FUNCTIONAL MEASUREMENT ERROR MODELS WITH REPLICATED DATA

We consider the construction and properties of influence functions in the context of functional measurement error models with replicated data. In these models estimates of the parameters can be affected both by the individual observations and the means of replicated observations. We show that influence function of the means of replicates on the estimate of regression coefficients can be only derived under the assumption that the variances of the errors are known, while one for the individual observations can be only derived simultaneously with their influence function on the estimators of the variances of the errors.     

Introduction: making the case for qualitative interviews

ABSTRACT

We introduce four papers comprising a Themed Section for this issue of the International Journal of Social Research Methodology, which together ‘Make the Case for Qualitative Interviews’. Here our aim is to show how this collection provides a timely contribution to key debates concerning the value of qualitative interviews, particularly as these are employed and analysed in much recent social scientific thinking. We explore ways to move beyond recent, sometimes constraining and occasionally dismissive, approaches to interviews in the social sciences through reframing and reconfiguring central questions germane to these debates. We also seek to challenge a broader neo-liberal trend towards valuing quantitative over depth qualitative research. Through this Introduction, and the collection of papers that follows, we seek to re-establish the value of qualitative interviews by shifting the focus from a preoccupation with what interviews can be said to do, towards questions centring on what can be done with interviews.     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.