Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes

We propose a sparse coefficient estimation and automated model selection procedure for autoregressive processes with heavy-tailed innovations based on penalized conditional maximum likelihood. Under mild moment conditions on the innovation processes, the penalized conditional maximum likelihood estimator satisfies a strong consistency, O_P(N^?1/2) consistency, and the oracle properties, where N is the sample size. We have the freedom in choosing penalty functions based on the weak conditions on them. Two penalty functions, least absolute shrinkage and selection operator and smoothly clipped average deviation, are compared. The proposed method provides a distribution-based penalized inference to AR models, which is especially useful when the other estimation methods fail or under perform for AR processes with heavy-tailed innovations [Feigin, Resnick. Pitfalls of fitting autoregressive models for heavy-tailed time series. Extremes. 1999;1:391–422]. A simulation study confirms our theoretical results. At the end, we apply our method to a historical price data of the US Industrial Production Index for consumer goods, and obtain very promising results.     

How to conduct a Narrative Policy Framework study

The Narrative Policy Framework has a growing number of researchers seeking to apply the framework in policy process scholarship. This article is intended to assist those interested in conducting an NPF study that is ‘clear enough to be wrong’ (Sabatier, 2000). While graduate programs offer critical methodological training, this article focuses on the specific application of the NPF to research inquiries about the role of policy narratives in the policy process. We approach our discussion by examining various decisions in the research process and include a detailed discussion of specifying the model and obtaining narrative data. We also point out areas for further investigation.     

Strategy as engagement: What organization strategy can learn from military strategy

Strategy process and practice research has illuminated the internal dynamics of strategy work – at the cost of backgrounding processes and practices that relate to engagement with external actors. In this conceptual paper, we argue for an extension of this body of work by shifting the focus of research from internal practices and processes towards externally oriented practices of engagement. We do so by critically building on the military strategy literature and develop the concept of strategy as engagement. This concept suggests understanding the role of strategy as bridge between policy and tactics; the importance of grand strategy as the making of policy; and the need to focus attention on tactics as distributed collective action. Thus, we contribute to strategy process and practice research through 1) extending its repertoire to practices of engagement and 2) broadening its epistemic foundation through a critical reading of military strategy.     

Time varying long memory parameter estimation for locally stationary long memory processes

The semiparametric estimators of time varying long memory parameter are investigated for locally stationary long memory processes. The GPH estimator and the local Whittle estimator are considered. Under some mild regularity assumptions, the weak consistency and the asymptotic normality of the estimators are obtained. The finite sample performance of the estimators is discussed through a small simulation study.     

Expansion and estimation of Lévy process functionals in nonlinear and nonstationary time series regression

In this article, we develop a series estimation method for unknown time-inhomogeneous functionals of Lévy processes involved in econometric time series models. To obtain an asymptotic distribution for the proposed estimators, we establish a general asymptotic theory for partial sums of bivariate functionals of time and nonstationary variables. These results show that the proposed estimators in different situations converge to quite different random variables. In addition, the rates of convergence depend on various factors rather than just the sample size. Finite sample simulations are provided to evaluate the finite sample performance of the proposed model and estimation method.     

Against the De Minimis Principle

According to the class of de minimis decision principles, risks can be ignored (or at least treated very differently from other risks) if the risk is sufficiently small. In this article, we argue that a de minimis threshold has no place in a normative theory of decision making, because the application of the principle will either recommend ignoring risks that should not be ignored (e.g., the sure death of a person) or it cannot be used by ordinary bounded and information-constrained agents.     

The serene student: Evaluating a group-based mindfulness training program for MSW students

ABSTRACT

Social work educators are exploring the benefits of mindfulness training for social work pedagogy. The authors evaluate the outcomes of a mindfulness program for MSW students. The authors assessed changes in mindfulness, self-compassion, affect, mood, and impairment due to emotional distress before and after the program, and at 4-month follow-up. The authors examine how the group process may facilitate the development of mindfulness and consider how mindfulness practices can support group development and student learning. Results demonstrate that mindfulness training supports student self-regulation and well-being and suggest that the active ingredients of the group process may play a role in mindfulness training outcomes.     

A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data

In in most cases, the distribution of communications is unknown and one may summarize social network communications with categorical attributes in a contingency table. Due to the categorical nature of the data and a large number of features, there are many parameters to be considered and estimated in the model. Hence, the accuracy of estimators decreases. To overcome the problem of high dimensionality and unknown communications distribution, multiple correspondence analysis is used to reduce the number of parameters. Then the rescaled data are studied in a Dirichlet model in which the parameters should be estimated. Moreover, two control charts, Hotelling’s T² and multivariate exponentially weighted moving average (MEWMA), are developed to monitor the parameters of the Dirichlet distribution. The performance of the proposed method is evaluated through simulation studies in terms of average run length criterion. Finally, the proposed method is applied to a real case.     

Risk Ranking of Antimicrobial‐Resistant Hazards Found in Meat in Switzerland

Human exposure to bacteria resistant to antimicrobials and transfer of related genes is a complex issue and occurs, among other pathways, via meat consumption. In a context of limited resources, the prioritization of risk management activities is essential. Since the antimicrobial resistance (AMR) situation differs substantially between countries, prioritization should be country specific. The objective of this study was to develop a systematic and transparent framework to rank combinations of bacteria species resistant to selected antimicrobial classes found in meat, based on the risk they represent for public health in Switzerland. A risk assessment model from slaughter to consumption was developed following the Codex Alimentarius guidelines for risk analysis of foodborne AMR. Using data from the Swiss AMR monitoring program, 208 combinations of animal species/bacteria/antimicrobial classes were identified as relevant hazards. Exposure assessment and hazard characterization scores were developed and combined using multicriteria decision analysis. The effect of changing weights of scores was explored with sensitivity analysis. Attributing equal weights to each score, poultry‐associated combinations represented the highest risk. In particular, contamination with extended‐spectrum β‐lactamase/plasmidic AmpC‐producing Escherichia coli in poultry meat ranked high for both exposure and hazard characterization. Tetracycline‐ or macrolide‐resistant Enterococcus spp., as well as fluoroquinolone‐ or macrolide‐resistant Campylobacter jejuni , ranked among combinations with the highest risk. This study provides a basis for prioritizing future activities to mitigate the risk associated with foodborne AMR in Switzerland. A user‐friendly version of the model was provided to risk managers; it can easily be adjusted to the constantly evolving knowledge on AMR.