An Empirical Bayes Inference for the von Mises Distribution

This paper develops an empirical Bayesian analysis for the von Mises distribution, which is the most useful distribution for statistical inference of angular data. A two-stage informative prior is proposed, in which the hyperparameter is obtained from the data in one of the stages. This empirical or approximate Bayes inference is justified on the basis of maximum entropy, and it eliminates the modified Bessel functions. An example with real data and a realistic prior distribution for the regression coefficients is considered via a Metropolis-within-Gibbs algorithm.     

Reduced-Bias Tail Index Estimators Under a Third-Order Framework

In this article, we are interested in the comparison, under a third-order framework, of classes of second-order, reduced-bias tail index estimators, giving particular emphasis to minimum-variance reduced-bias estimators of the tail index γ. The full asymptotic distributional properties of the proposed classes are derived under a third-order framework and the estimators are compared with other alternatives, not only asymptotically, but also for finite samples through Monte Carlo techniques. An application to the log-exchange rates of the Euro against the USA Dollar is also provided.     

A note on testing for nonstationarity in autoregressive processes with level dependent conditional heteroskedasticity

In this paper, we investigate the empirical distribution and the statistical properties of maximum likelihood (ML) unit-root t-statistics computed from data sampled from a first-order autoregressive (AR) process with level-dependent conditional heteroskedasticity (LDCH). This issue is of particular importance for applications on interest rate time-series. Unfortunately, the extent of the technical complexity related associated to LDCH patterns does not offer a feasible theoretical analysis, and there is no formal knowledge about the finite-sample size and power behaviour or the ML test for this context. Our analysis provides valuable guidelines for applied work and directions for future work.     

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Resumen

Presentamos una nueva escala para medir el estrés de aculturación de los autóctonos ante la inmigración, concretamente sudamericana. Hemos partido del concepto de amenaza percibida tomado de la Teoría Integrada de la Amenaza (Stephan y Stephan, 1996). Un análisis de componentes principales realizado con una muestra de 770 participantes ofrece una estructura compuesta por dos dimensiones: estrés de interacción y estrés por recursos. La escala final está formada por 16 ítems más tres de control de la deseabilidad social. Los análisis de fiabilidad han encontrado unos índices de consistencia interna muy satisfactorios, de.91 para la escala general de estrés de aculturación y de.91 y.88 para las dos subescalas. Los datos obtenidos permiten ofrecer un instrumento de medida que constituye una contribución en el estudio de los procesos de aculturación.     

Analysis of an European union election using principal component analysis

While studying the results from one European Parliament election, the question of principal component analysis (PCA) suitability for this kind of data was raised. Since multiparty data should be seen as compositional data (CD), the application of PCA is inadvisable and may conduct to ineligible results. This work points out the limitations of PCA to CD and presents a practical application to the results from the European Parliament election in 2004. We present a comparative study between the results of PCA, Crude PCA and Logcontrast PCA (Aitchison in: Biometrika 70:57–61, 1983; Kucera, Malmgren in: Marine Micropaleontology 34:117–120, 1998). As a conclusion of this study, and concerning the mentioned data set, the approach which produced clearer results was the Logcontrast PCA. Moreover, Crude PCA conducted to misleading results since nonlinear relations were presented between variables and the linear PCA proved, once again, to be inappropriate to analyse data which can be seen as CD.     

Double-sampling control charts for attributes

In this article, we propose a double-sampling (DS) np control chart. We assume that the time interval between samples is fixed. The choice of the design parameters of the proposed chart and also comparisons between charts are based on statistical properties, such as the average number of samples until a signal. The optimal design parameters of the proposed control chart are obtained. During the optimization procedure, constraints are imposed on the in-control average sample size and on the in-control average run length. In this way, required statistical properties can be assured. Varying some input parameters, the proposed DS np chart is compared with the single-sampling np chart, variable sample size np chart, CUSUM np and EWMA np charts. The comparisons are carried out considering the optimal design for each chart. For the ranges of parameters considered, the DS scheme is the fastest one for the detection of increases of 100% or more in the fraction non-conforming and, moreover, the DS np chart is easy to operate.     

Risk perception and post-formation governance in international joint ventures in Taiwan: The perspective of the foreign partner

This paper draws on a risk analysis framework in order to develop a systematic understanding of the risks perceived by partners, and to investigate the implications of risk perception for the configuration of control, in the post-formation governance of international joint ventures. The key variables in this framework consist of six situational factors which are considered as potential antecedents of perceived risk: conflict, opportunism, cultural difference, dependence, partner fit, and ownership share; partner's perception of risk; and post-formation governance as a set of outcome variables. The framework is tested using a sample of international joint ventures located in Taiwan. The findings show that conflicts between partners, opportunistic behavior by the local partner, cultural differences, and perceived partner misfit are related to foreign partners' risk perceptions. This study suggests that when foreign partners face likely performance and partnership risks after an IJV is established, they tend to resort to tighter post-formation governance measures in order to increase or maintain their confidence in their joint ventures.     

Weibull and generalised exponential overdispersion models with an application to ozone air pollution

We consider the problem of estimating the mean and variance of the time between occurrences of an event of interest (inter-occurrences times) where some forms of dependence between two consecutive time intervals are allowed. Two basic density functions are taken into account. They are the Weibull and the generalised exponential density functions. In order to capture the dependence between two consecutive inter-occurrences times, we assume that either the shape and/or the scale parameters of the two density functions are given by auto-regressive models. The expressions for the mean and variance of the inter-occurrences times are presented. The models are applied to the ozone data from two regions of Mexico City. The estimation of the parameters is performed using a Bayesian point of view via Markov chain Monte Carlo (MCMC) methods.     

The exponential COM-Poisson distribution

The Conway-Maxwell Poisson (COMP) distribution as an extension of the Poisson distribution is a popular model for analyzing counting data. For the first time, we introduce a new three parameter distribution, so-called the exponential-Conway-Maxwell Poisson (ECOMP) distribution, that contains as sub-models the exponential-geometric and exponential-Poisson distributions proposed by Adamidis and Loukas (Stat Probab Lett 39:35?C42, 1998) and Ku? (Comput Stat Data Anal 51:4497?C4509, 2007), respectively. The new density function can be expressed as a mixture of exponential density functions. Expansions for moments, moment generating function and some statistical measures are provided. The density function of the order statistics can also be expressed as a mixture of exponential densities. We derive two formulae for the moments of order statistics. The elements of the observed information matrix are provided. Two applications illustrate the usefulness of the new distribution to analyze positive data.     

Regression models for time-varying extremes

A common approach to modelling extreme data are to consider the distribution of the exceedance value over a high threshold. This approach is based on the distribution of excess, which follows the generalized Pareto distribution (GPD) and has shown to be adequate for this type of situation. As with all data involving analysis in time, excesses above a threshold may also vary and suffer from the influence of covariates. Thus, the GPD distribution can be modelled by entering the presence of these factors. This paper presents a new model for extreme values, where GPD parameters are written on the basis of a dynamic regression model. The estimation of the model parameters is made under the Bayesian paradigm, with sampling points via MCMC. As with environmental data, behaviour data are related to other factors such as time and covariates such as latitude and distance from the sea. Simulation studies have shown the efficiency and identifiability of the model, and applying real rain data from the state of Piaui, Brazil, shows the advantage in predicting and interpreting the model against other similar models proposed in the literature.