A comparison of L-, LQ-, TL-moment and maximum likelihood high quantile estimates of the GPD and GEV distribution

In this article, L-moments, LQ-moments and TL-moments of the generalized Pareto and generalized extreme-value distributions are derived up to the fourth order. The first three L-, LQ- and TL-moments are used to obtain estimators of their parameters. Performing a simulation study, high-quantile estimates based on L-, LQ-, and TL-moments are compared to the maximum likelihood estimate with respect to their sample mean squared error. This consists of identifying an optimal combination of parameters α and p both considered in the range [0, 0.5] for estimating quantiles by LQ-moments. The results show L-moment and maximum likelihood methods outperform other methods.     

紧急状态、资本主义和法治

本文以详尽的历史事实为依据,对西方资本主义国家中的紧急状态及所谓的法治进行了深入的研究与批评,以此为基础,对西方左派在革命战略方面的转变之原因作出了说明.     

Partially linear censored quantile regression

Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate. In many cases however, greater flexibility is desired to go beyond the usual multiple regression paradigm. One area of common interest is that of partially linear models: one (or more) of the explanatory covariates are assumed to act on the response through a non-linear function. Here the CRQ approach of Portnoy (J Am Stat Assoc 98:1001–1012, 2003) is extended to this partially linear setting. Basic consistency results are presented. A simulation experiment and unemployment example justify the value of the partially linear approach over methods based on the Cox proportional hazards model and on methods not permitting nonlinearity.     

The Brazilian Multinationals' Approaches to Innovation

Brazilian multinationals, born in a country where the environment is non-conducive to the scientific breakthrough type of innovation so stimulated in other countries, are increasingly expanding in international markets, where innovativeness is an intrinsic component of competitiveness. Aiming to study Brazilian multinationals' approaches to innovation, a new analytical framework was developed assuming that the internationalization process relies on the firm's innovative capability. In turn, innovative capabilities are derived from core competences and competence formation at firm level is influenced by the characteristics of the national environment. A survey involving 61 Brazilian multinationals led to the identification of four approaches to innovation, the competences that enable each one of them and the country-of-origin effects over competence development. That led to the explanation of why firms that do not show the expected strength in R&D, but are able to combine skillfully their organizational competences, manage to develop innovative capabilities which allow them to internationalize successfully. The overall outcome suggests that the dynamic relationships among institutions – competences – innovation – internationalization are setting new grounds for the international expansion of Brazilian firms.     

Meaning and Verbal Behavior in Skinner's Work from 1934 to 1957

This paper explores the historical development of Skinner''s treatment of meaning from 1930 to 1957. Twelve papers published between 1934 and 1957, and parts of The Behavior of Organisms and Science and Human Behavior related to verbal behavior, were analyzed. Before 1945 meaning was taken as a property of the verbal response, and from 1945 on, meaning was supposed to be found among the determiners of a verbal response. We argue that these different conceptions of meaning were related to distinct aspects of Skinner''s explanatory system. Finally, the notion of meaning presented by Skinner in 1945 is related to the theoretical breakthrough represented by Skinner''s assertion of the three-term contingency. This idea permitted verbal behavior to be analyzed in terms of its functions.     

Efficient Bayesian inference for COM-Poisson regression models

COM-Poisson regression is an increasingly popular model for count data. Its main advantage is that it permits to model separately the mean and the variance of the counts, thus allowing the same covariate to affect in different ways the average level and the variability of the response variable. A key limiting factor to the use of the COM-Poisson distribution is the calculation of the normalisation constant: its accurate evaluation can be time-consuming and is not always feasible. We circumvent this problem, in the context of estimating a Bayesian COM-Poisson regression, by resorting to the exchange algorithm, an MCMC method applicable to situations where the sampling model (likelihood) can only be computed up to a normalisation constant. The algorithm requires to draw from the sampling model, which in the case of the COM-Poisson distribution can be done efficiently using rejection sampling. We illustrate the method and the benefits of using a Bayesian COM-Poisson regression model, through a simulation and two real-world data sets with different levels of dispersion.