Detection and Interpretation of a Multivariate Signal Using Combined Charts

A control procedure is presented in this article that is based on jointly using two separate control statistics in the detection and interpretation of signals in a multivariate normal process. The procedure detects the following three situations: (i) a mean vector shift without a shift in the covariance matrix; (ii) a shift in process variation (covariance matrix) without a mean vector shift; and (iii) both a simultaneous shift in the mean vector and covariance matrix as the result of a change in the parameters of some key process variables. It is shown that, following the occurrence of a signal on either of the separate control charts, the values from both of the corresponding signaling statistics can be decomposed into interpretable elements. Viewing the two decompositions together helps one to specifically identify the individual components and associated variables that are being affected. These components may include individual means or variances of the process variables as well as the correlations between or among variables. An industrial data set is used to illustrate the procedure.     

Re-examining two tests for bivariate normality

Many goodness of fit tests for bivariate normality are not rigorous procedures because the distributions of the proposed statistics are unknown or too difficult to manipulate. Two familiar examples are the ring test and the line test. In both tests the statistic utilized generally is approximated by a chi-square distribution rather than compared to its known beta distribution. These two procedures are re-examined and re-evaluated in this paper. It is shown that the chi-square approximation can be too conservative and can lead to unnecessary

rejection of normality.     

Bayesian Forecasting With Stable Seasonal Patterns

A multiplicative seasonal forecasting model for cumulative events in which, conditional on end- of-season totals being given and seasonal shape being known, it is shown that events occurring within the season are multinomially distributed is presented. The model uses the information contained in the arrival of new events to obtain a posterior distribution for end-of-season totals. Bayesian forecasts are obtained recursively in two stages: first, by predicting the expected number and variance of event counts in future intervals within the remaining season, and then by predicting revised means and variances for end-of-season totals based on the most recent forecast error.     

Correlation-Type Goodness of Fit Test for Extreme Value Distribution Based on Simultaneous Closeness

In reliability studies, one typically would assume a lifetime distribution for the units under study and then carry out the required analysis. One popular choice for the lifetime distribution is the family of two-parameter Weibull distributions (with scale and shape parameters) which, through a logarithmic transformation, can be transformed to the family of two-parameter extreme value distributions (with location and scale parameters). In carrying out a parametric analysis of this type, it is highly desirable to be able to test the validity of such a model assumption. A basic tool that is useful for this purpose is a quantile–quantile (QQ) plot, but in its use, the issue of the choice of plotting position arises. Here, by adopting the optimal plotting points based on Pitman closeness criterion proposed recently by Balakrishnan et al. (2010b Balakrishnan , N. , Davies , K. F. , Keating , J. P. , Mason , R. L. ( 2010b ). Computation of optimal plotting points based on Pitman Closeness with an application to goodness of fit for location-scale families. Submitted to Computational Statistics & Data Analysis.  [Google Scholar]), and referred to as simultaneous closeness probability (SCP) plotting points, we propose a correlation-type goodness of fit test for the extreme value distribution. We compute the SCP plotting points for various sample sizes and use them to determine the mean, standard deviation and critical values for the proposed correlation-type test statistic. Using these critical values, we carry out a power study, similar to the one carried out by Kinnison (1989 Kinnison , R. ( 1989 ). Correlation coefficient goodness of fit test for extreme value distribution . The American Statistician 43 : 98 – 100 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), through which we demonstrate that the use of SCP plotting points results in better power than with the use of mean ranks as plotting points and nearly the same power as with the use of median ranks. We then demonstrate the use of the SCP plotting points and the associated correlation-type test for Weibull analysis with an illustrative example. Finally, for the sake of comparison, we also adapt two statistics proposed by Gan and Koehler (1990 Gan , F. F. , Koehler , K. J. ( 1990 ). Goodness of fit based on P-P probability plots . Technometrics 32 : 289 – 303 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), in the context of probability–probability (PP) plots, based on SCP plotting points and compare their performance to those based on mean ranks. The empirical study also reveals that the tests from the QQ plot have better power than those from the PP plot.     

Planning personnel retraining: column generation heuristics

Retraining of staff is a compulsory managerial function in many organisations and often requires planning for a large number of employees. The large scale of this problem and various restrictions on the resultant assignment to classes make this planning challenging. The paper presents a complexity analysis of this problem together with linear and nonlinear mathematical programming formulations. Three different column generation based optimisation procedures and a large neighbourhood search procedure, incorporating column generation, are compared by means of computational experiments. The experiments used data typical to large electricity distributors.     

Rose Lindsey,John Mohan,Elizabeth Metcalfe,Sarah Bulloch: Continuity and Change in Voluntary Action: Patterns,Trends and Understandings

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -     

The Death Penalty and Historical Change in Spain

This article studies the long duration of the death penalty in Spain until its abolition in the Constitution of 1978. After analysing the plurality of theoretical approaches and possibilities offered by archival sources and specialised historiography (particularly those produced by specialists in the history of law and social history), I synthesise the Spanish answers to the major questions posed in the international historiographical debate on this issue. I then review the formality and the religious and juridical content of these “ceremonies of torment” in order to understand the scope of the practice of the death penalty in processes of social change: what did political power transmit from the scaffolds and what type of social impact was generated by public executions? How did the institutions that exercised the death penalty evolve? Why, during the transition from the Old Regime to the liberal state, was the death penalty used more regularly than before? How many prisoners were in fact executed, for what crimes, and using what procedures and techniques? Finally, after confirming that in the decades straddling the nineteenth and twentieth centuries the death penalty was on the decline, being counteracted by abolitionist discourse in the field of penal sentencing, I examine the functions played by political executions in the repressive dynamics of the Civil War and Francoism.     

Weighted Approximations to Continuous Time Martingales with Applications

A weighted approximation to a sequence of continuous time martingales by a time transformed Wiener process is established. The basic tool of proof is the Skorohod imbedding for martingale difference sequences. As an application of the main result a useful weighted approximation to the randomly weighted uniform empirical process is derived. A number of other applications are also discussed.     

Electricity consumption changes following solar adoption: Testing for a solar rebound

We use household-level data to explore residential electricity use patterns following installation of solar panels. Solar adoption leads to an increase in total electricity consumption relative to a matched non-adopting control group. Our point estimate translates to a rebound effect of 28.5%, suggesting that nearly a third of the electricity produced by a customer's solar panels is used for increased energy services, rather than reduced grid electricity consumption. We explore several potential drivers of an increase in electricity consumption. These results have important implications for electricity planning and policy, suggesting that rooftop solar stimulates additional demand for electricity.