Conflict management, Part 1. Conflict management checklist: a diagnostic tool for assessing conflict in organizations

Complex interpersonal conflicts are inevitable in the high speed, high stakes, pressured work of health care. Poorly managed, conflict saps productivity, erodes trust, and spawns additional disputes. Well managed, conflict can enhance the self-confidence and self-esteem of the parties, build relationships, and engender creative solutions beyond expectations. Just as thoughtful differential diagnosis precedes optimum treatment in the doctor-patient relationship, management of conflict is greatly enhanced when preceded by careful assessment. In the first of two articles, the authors present a diagnostic approach, the Conflict Management Checklist, to increase self-awareness and decrease anxiety around conflict.     

Improving performance in a contracted physician network

Health care organizations face significant performance challenges. Achieving desired results requires the highest level of partnership with independent physicians. Tufts Health Plan invited medical directors of its affiliated groups to participate in a leadership development process to improve clinical, service, and business performance. The design included performance review, gap analysis, priority setting, improvement work plans, and defining the optimum practice culture. Medical directors practiced core leadership capabilities, including building a shared context, getting physician buy-in, and managing outliers. The peer learning environment has been sustained in redesigned medical directors' meetings. There has been significant performance improvement in several practices and enhanced relations between the health plan and medical directors.     

A hazard model of the probability of medical school drop-out in the UK

Summary.  From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons.     

A note on the adaptive control of false discovery rates

Summary.  The use of a fixed rejection region for multiple hypothesis testing has been shown to outperform standard fixed error rate approaches when applied to control of the false discovery rate. In this work it is demonstrated that, if the original step-up procedure of Benjamini and Hochberg is modified to exercise adaptive control of the false discovery rate, its performance is virtually identical to that of the fixed rejection region approach. In addition, the dependence of both methods on the proportion of true null hypotheses is explored, with a focus on the difficulties that are involved in the estimation of this quantity.     

A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy

When elections are close in time, voters may stick to their preferred party or chose a different option for several reasons; reliable estimates of the amount of transitions across the available options can allow to answer a number of relevant questions about electoral behaviour. We describe a modified version of the model due to Brown and Payne (J Am Stat Assoc 81:453–460, 1986) and argue that it is based on simple, yet realistic, assumptions with a direct interpretation in terms of individual behaviour and compares well with other models proposed more recently. We apply the model to an Italian borough where, during June 2009, two elections were held simultaneously and a runoff took place two weeks later. Estimates of the joint distribution of voters between the European Parliament election and the other two elections provide evidence of substantially different kinds of voting behaviour which, given the specific context, we interpret in the light of the recent literature on the subject.     

Nonparametric estimation of the derivatives of a density by the method of wavelet for mixing sequences

The problem of estimation of the derivative of a probability density f is considered, using wavelet orthogonal bases. We consider an important kind of dependent random variables, the so-called mixing random variables and investigate the precise asymptotic expression for the mean integrated error of the wavelet estimators. We show that the mean integrated error of the proposed estimator attains the same rate as when the observations are independent, under certain week dependence conditions imposed to the {X _i }, defined in {Ω, N, P}.     

Spectral estimation for locally stationary time series with missing observations

Time series arising in practice often have an inherently irregular sampling structure or missing values, that can arise for example due to a faulty measuring device or complex time-dependent nature. Spectral decomposition of time series is a traditionally useful tool for data variability analysis. However, existing methods for spectral estimation often assume a regularly-sampled time series, or require modifications to cope with irregular or ‘gappy’ data. Additionally, many techniques also assume that the time series are stationary, which in the majority of cases is demonstrably not appropriate. This article addresses the topic of spectral estimation of a non-stationary time series sampled with missing data. The time series is modelled as a locally stationary wavelet process in the sense introduced by Nason et al. (J. R. Stat. Soc. B 62(2):271–292, 2000) and its realization is assumed to feature missing observations. Our work proposes an estimator (the periodogram) for the process wavelet spectrum, which copes with the missing data whilst relaxing the strong assumption of stationarity. At the centre of our construction are second generation wavelets built by means of the lifting scheme (Sweldens, Wavelet Applications in Signal and Image Processing III, Proc. SPIE, vol. 2569, pp. 68–79, 1995), designed to cope with irregular data. We investigate the theoretical properties of our proposed periodogram, and show that it can be smoothed to produce a bias-corrected spectral estimate by adopting a penalized least squares criterion. We demonstrate our method with real data and simulated examples.     

Sequential Monte Carlo for rare event estimation

This paper discusses a novel strategy for simulating rare events and an associated Monte Carlo estimation of tail probabilities. Our method uses a system of interacting particles and exploits a Feynman-Kac representation of that system to analyze their fluctuations. Our precise analysis of the variance of a standard multilevel splitting algorithm reveals an opportunity for improvement. This leads to a novel method that relies on adaptive levels and produces, in the limit of an idealized version of the algorithm, estimates with optimal variance. The motivation for this theoretical work comes from problems occurring in watermarking and fingerprinting of digital contents, which represents a new field of applications of rare event simulation techniques. Some numerical results show performance close to the idealized version of our technique for these practical applications.     

Statistical concepts of a priori and a posteriori risk classification in insurance

Everyday we face all kinds of risks, and insurance is in the business of providing us a means to transfer or share these risks, usually to eliminate or reduce the resulting financial burden, in exchange for a predetermined price or tariff. Actuaries are considered professional experts in the economic assessment of uncertain events, and equipped with many statistical tools for analytics, they help formulate a fair and reasonable tariff associated with these risks. An important part of the process of establishing fair insurance tariffs is risk classification, which involves the grouping of risks into various classes that share a homogeneous set of characteristics allowing the actuary to reasonably price discriminate. This article is a survey paper on the statistical tools for risk classification used in insurance. Because of recent availability of more complex data in the industry together with the technology to analyze these data, we additionally discuss modern techniques that have recently emerged in the statistics discipline and can be used for risk classification. While several of the illustrations discussed in the paper focus on general, or non-life, insurance, several of the principles we examine can be similarly applied to life insurance. Furthermore, we also distinguish between a priori and a posteriori ratemaking. The former is a process which forms the basis for ratemaking when a policyholder is new and insufficient information may be available. The latter process uses additional historical information about policyholder claims when this becomes available. In effect, the resulting a posteriori premium allows one to correct and adjust the previous a priori premium making the price discrimination even more fair and reasonable.     

Decomposing the complete graph into dodecahedra

We prove that the complete graph K_v can be decomposed into dodecahedra if and only if v≡ 1, 16, 25 or 40 (mod 60), v≠16.