Multivariate mixtures of Erlangs for density estimation under censoring

Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets.     

The beta exponentiated Weibull distribution

The Weibull distribution is one of the most important distributions in reliability. For the first time, we introduce the beta exponentiated Weibull distribution which extends recent models by Lee et al. [Beta-Weibull distribution: some properties and applications to censored data, J. Mod. Appl. Statist. Meth. 6 (2007), pp. 173–186] and Barreto-Souza et al. [The beta generalized exponential distribution, J. Statist. Comput. Simul. 80 (2010), pp. 159–172]. The new distribution is an important competitive model to the Weibull, exponentiated exponential, exponentiated Weibull, beta exponential and beta Weibull distributions since it contains all these models as special cases. We demonstrate that the density of the new distribution can be expressed as a linear combination of Weibull densities. We provide the moments and two closed-form expressions for the moment-generating function. Explicit expressions are derived for the mean deviations, Bonferroni and Lorenz curves, reliability and entropies. The density of the order statistics can also be expressed as a linear combination of Weibull densities. We obtain the moments of the order statistics. The expected information matrix is derived. We define a log-beta exponentiated Weibull regression model to analyse censored data. The estimation of the parameters is approached by the method of maximum likelihood. The usefulness of the new distribution to analyse positive data is illustrated in two real data sets.     

Mixture inverse Gaussian distributions and its transformations,moments and applications

Skewed models are important and necessary when parametric analyses are carried out on data. Mixture distributions produce widely flexible models with good statistical and probabilistic properties, and the mixture inverse Gaussian (MIG) model is one of those. Transformations of the MIG model also create new parametric distributions, which are useful in diverse situations. The aim of this paper is to discuss several aspects of the MIG distribution useful for modelling positive data. We specifically discuss transformations, the derivation of moments, fitting of models, and a shape analysis of the transformations. Finally, real examples from engineering, environment, insurance, and toxicology are presented for illustrating some of the results developed here. Three of the four data sets, which have arisen from the consulting work of the authors, are new and have not been previously analysed. All these examples display that the empirical fit of the MIG distribution to the data is very good.     

Experience with an electricity market simulation tool

A successful experience to train power producers on pool-based electricity markets presented. Trainees are arranged in groups and assigned, at random, a set of generators. They play the role of power producers and compete against each other with the target of maximizing their own profits. They have to bid in terms of price and quantity to try to sell energy to the consumers through the market. The trainer plays the role of the market operator. A market simulator and a computer communication network make it possible to simulate the actual functioning of a pool-based electricity market. The simulator is based on the widely used software package MATLAB, and a computer communication net, which can be replaced by the WWW.     

Small Steps that Matter: Incremental Learning,Slack Resources and Organizational Performance

This paper examines the antecedents, consequences and moderators of incremental learning capabilities, understood as an organization's ability to gradually adapt and expand its knowledge base. Conceptualized as a dynamic capability, incremental learning is expected to be a vital driver of organizational adaptation. As dynamic capabilities consist of bundles of relatively stable routines, it is proposed that an organization's level of incremental learning capabilities will be highly persistent over time. It is also argued that building and exercising incremental learning capabilities is resource intensive and will as such tend to rely on the availability of sufficient slack resources. Last, it is suggested that incremental learning will be positively related to organizational performance, especially when the underlying business model is labour rather than capital intensive. To test these theoretical ideas, the authors draw on extensive panel data from all public non‐specialist hospitals in England and find broad support for their hypotheses.     

CAN WE INFER HOSPITAL QUALITY FROM MEDICAL GRADUATES’ RESIDENCY CHOICES?

This paper uses a novel approach to infer hospital technical quality from revealed preferences over residency programs. Specifically, we use Spanish medical graduates’ residency choices made from 1995 to 2000. We start by estimating a model of medical graduates preferences that controls for hospital, proximity, specialty, and gender effects. We interpret the coefficients on the hospital dummy variables as measures of medical graduates’ preferences over hospitals. Our results show that graduates do indeed discriminate between hospitals and that their preferences correlate with hospital‐specific covariates arguably related to hospital training quality. We then show that preferences from medical graduates are positively and statistically significantly correlated with risk‐adjusted hospital rankings based on five alternative outcome measures. Finally, we construct reputation scores for each hospital using news story counts in three media outlets and find that medical graduates’ preferences are especially valuable for inference of hospital technical quality of care as they do not simply reflect well known reputation.     

A Robust Model of Bubbles With Multidimensional Uncertainty

Observers often interpret boom–bust episodes in asset markets as speculative frenzies where asymmetrically informed investors buy overvalued assets hoping to sell to a greater fool before the crash. Despite its intuitive appeal, however, this notion of speculative bubbles has proven difficult to reconcile with economic theory. Existing models have been criticized on the basis that they assume irrationality, that prices are somewhat unresponsive to sales, or that they depend on fragile, knife‐edge restrictions. To address these issues, I construct a rational version of Abreu and Brunnermeier (2003), where agents invest growing endowments into an asset, fueling appreciation and eventual overvaluation. Riding bubbles is optimal as long as the growth rate of the bubble and the probability of selling before the crash are high enough. This probability increases with the amount of noise in the economy, as random short‐term fluctuations make it difficult for agents to infer information from prices.     

Higher Order Uncertainty and Information: Static and Dynamic Games

Weinstein and Yildiz (2007) have shown that in static games, only very weak predictions are robust to perturbations of higher order beliefs. These predictions are precisely those provided by interim correlated rationalizability (ICR). This negative result is obtained under the assumption that agents have no information on payoffs. This assumption is unnatural in many settings. It is therefore natural to ask whether Weinstein and Yildiz's results remain true under more general information structures. This paper characterizes the “robust predictions” in static and dynamic games, under arbitrary information structures. This characterization is provided by an extensive form solution concept: interim sequential rationalizability (ISR). In static games, ISR coincides with ICR and does not depend on the assumptions on agents' information. Hence the “no information” assumption entails no loss of generality in these settings. This is not the case in dynamic games, where ISR refines ICR and depends on the details of the information structure. In these settings, the robust predictions depend on the assumptions on agents' information. This reveals a hitherto neglected interaction between information and higher order uncertainty, raising novel questions of robustness.