Financial Globalisation,State Autonomy and Modern Financial Instruments: The Case of Brazil

This article examines the impact of innovative financial instruments on the relationship between states and financial markets. Using the example of Brazil in the period after 2000, this paper argues that growth in credits default swaps and inflation-linked bond markets have had a tangible impact on this relationship. Growth in these markets has afforded the Brazilian state more autonomy and lessened the normative power of financial markets. This paper shows that developments in global financial markets do not always result in a diminution of state power.

Este artículo examina el impacto de los instrumentos financieros innovadores en la relación entre los estados y los mercados financieros. Tomando el ejemplo del Brasil en el período después del 2000, este artículo sostiene que el crecimiento en los swaps en el incumplimiento crediticio y el mercado de bonos estructurados vinculados a la inflación, han tenido un impacto tangible en esta relación. La habilidad de pasar el riesgo de impago a mercados financieros más amplios, ha significado que más instituciones estén dispuestas a prestarle al Brasil y como consecuencia, la relación del país con la comunidad financiera ha cambiado. El crecimiento en estos mercados ha provisto al estado brasileño de más autonomía y reducido el poder normativo de los mercados financieros. Este artículo indica que los desarrollos en los mercados financieros globales, no siempre resultan en una disminución del poder estatal.

     

NORC Supportive Services Model Implementation and Community Capacity

As community residents age, service delivery systems may not be equipped to meet their changing needs. The Naturally Occurring Retirement Community (NORC) model has emerged as a strategy to utilize existing resources and develop a comprehensive approach to maintaining older adult functional status. However, little has been written about how community capacity influences NORC implementation. A community capacity framework was used to analyze the themes that emerged from participant and service provider interviews at two diverse NORC sites. The findings revealed that perceptions of a sense of community and partnership management influenced the NORC experience for participants and providers.     

Demographic and Socio-economic Influences on Recent British Marital Breakdown Patterns

The advantages of large-scale multi-purpose surveys compared with official divorce records for examining marital breakdown are assessed, and the extent of under-recording of the concept of breakdown in the latter source is estimated. Demographic and socio-economic differentials in breakdown are examined and the former are found to be generally more powerful. A proportional-hazards life-table model is used to establish the impact of childlessness on divorce in a more satisfactory way than hitherto. Among fertile couples, the length of the first birth interval is found to be particularly important as a risk factor influencing breakdown. Controlling for demographic factors, such as age at marriage and fertility status, is shown to modify the observed crude differences between social classes. Housing tenure and personal factors associated with the couple's individual circumstances are more important than social class in explaining marital breakdown, with age at marriage retaining a strongly persisting and remarkably constant effect whatever other variables are included in the analysis.     

Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications

Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.     

Identifying Nonlinear Relationships in Regression using the ACE Algorithm

This paper introduces an alternating conditional expectation (ACE) algorithm: a non-parametric approach for estimating the transformations that lead to the maximal multiple correlation of a response and a set of independent variables in regression and correlation analysis. These transformations can give the data analyst insight into the relationships between these variables so that this can be best described and non-linear relationships uncovered. Using the Bayesian information criterion (BIC), we show how to find the best closed-form approximations for the optimal ACE transformations. By means of ACE and BIC, the model fit can be considerably improved compared with the conventional linear model as demonstrated in the two simulated and two real datasets in this paper.     

The changing ideology and practice of modern policing

David Bayley and Jerome Skolnick The New Blue Line, Police Innovation in Six American Cities
Richard Kinsey, John Lea and Jock Young Losing the Fight Against Crime
Robert Reiner The Politics of the Police
Mollie Weatheritt Innovations in Policing     

PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES

Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients [11]. An individualized treatment rule is a decision rule that recommends treatment according to patient characteristics. We consider the use of clinical trial data in the construction of an individualized treatment rule leading to highest mean response. This is a difficult computational problem because the objective function is the expectation of a weighted indicator function that is non-concave in the parameters. Furthermore there are frequently many pretreatment variables that may or may not be useful in constructing an optimal individualized treatment rule yet cost and interpretability considerations imply that only a few variables should be used by the individualized treatment rule. To address these challenges we consider estimation based on l(1) penalized least squares. This approach is justified via a finite sample upper bound on the difference between the mean response due to the estimated individualized treatment rule and the mean response due to the optimal individualized treatment rule.     

Variation in Cancer Risk Estimates for Exposure to Powerline Frequency Electromagnetic Fields: A Meta-Analysis Comparing EMF Measurement Methods

We used meta-analysis to synthesize the findings from eleven case-control studies on cancer risks in humans exposed to 50-60 Hertz powerline electromagnetic fields (EMFs). Pooled estimates of risk are derived for different EMF measurement methods and types of cancer. EMF measurement methods are classified as: wiring configuration codes, distance to power distribution equipment, spot measurements of magnetic fields, and calculated indices based on distance to power distribution equipment and historic load data. Pooled odds ratios depicting the risk of cancer by each measurement type are presented for all cancers combined, leukemia for all age groups and childhood leukemia. The wire code measurement technique was associated with a significantly increased risk for all three cancer types, while spot measures consistently showed non-significant odds ratios. Distance measures and the calculated indices produced risk estimates which were significant only for leukemia.     

Anomaly and Novelty detection for robust semi-supervised learning

Statistics and Computing - Three important issues are often encountered in Supervised and Semi-Supervised Classification: class memberships are unreliable for some training units (label noise), a...