Convex combinations of long memory estimates from different sampling rates

Convex combinations of long memory estimates using the same data observed at different sampling rates can decrease the standard deviation of the estimates, at the cost of inducing a slight bias. The convex combination of such estimates requires a preliminary correction for the bias observed at lower sampling rates, reported by Souza and Smith (2002). Through Monte Carlo simulations, we investigate the bias and the standard deviation of the combined estimates, as well as the root mean squared error (RMSE), which takes both into account. While comparing the results of standard methods and their combined versions, the latter achieve lower RMSE, for the two semi-parametric estimators under study (by about 30% on average for ARFIMA(0,d,0) series).

Understanding the critical distance in sparse distributed memory

Modelos de tomada de decisão necessitam refletir os aspectos da psi- cologia humana. Com este objetivo, este trabalho é baseado na Sparse Distributed Memory (SDM), um modelo psicologicamente e neuro- cientificamente plausível da memória humana, publicado por Pentti Kanerva, em 1988. O modelo de Kanerva possui um ponto crítico: um item de memória aquém deste ponto é rapidamente encontrado, e items além do ponto crítico não o são. Kanerva calculou este ponto para um caso especial com um seleto conjunto de parâmetros (fixos). Neste trabalho estendemos o conhecimento deste ponto crítico, através de simulações computacionais, e analisamos o comportamento desta “Critical Distance” sob diferentes cenários: em diferentes dimensões; em diferentes números de items armazenados na memória; e em diferentes números de armazenamento do item. Também é derivada uma função que, quando minimizada, determina o valor da “Critical Distance” de acordo com o estado da memória. Um objetivo secundário do trabalho é apresentar a SDM de forma simples e intuitiva para que pesquisadores de outras áreas possam imaginar como ela pode ajudá-los a entender e a resolver seus problemas.

The consequences of the culture aspect and the power distance in the execution of global strategies in multinational companies in Brazil

One of the challenges presented by the current conjecture in Global Companies is to recognize and understand that the culture and levels in structure of the Power Distance in Organizations in different countries contribute, significantly, toward the failure or success of their strategies. The alignment between the implementation and execution of new strategies for projects intended for the success of the Organization as a whole, rather than as an individual part thereof, is an important step towards reducing the impacts of Power Distance (PDI) on the success of business strategies. A position at odds with this understanding by Companies creates boundaries that increase organizational chasms, also taking into consideration relevant aspects such as, FSAs (Firm-Specific Advantages) and CSAs (Country-Specific Advantages). It is also important that the Organizations based in countries or regions of low Power Distance (PDI) between its individuals be more flexible and prepared to ask and to hear the suggestions from Regional and Local Offices. Thus, the purpose of this study is to highlight the elements of effective strategy implementation considering the relevant aspects at all levels of global corporate culture that justify the influences of power distance when implementing new strategies and also to minimize the impacts of this internal business relationship. This study also recognizes that other corporate and cultural aspects are relevant for the success of business strategies so consider, for instance, the lack of alignment between global and regional/local organizations, the need for competent leadership resources, as well as the challenges that indicate the distance between the hierarchical levels ─ Headquarters and Regional Office ─ as some of the various causes that prevent the successful execution of global strategies. Finally, we show that the execution of the strategy cannot be treated as a construction solely created by the Headquarters or by only one Board and that it needs to be understood as a system aimed at interacting with the surroundings.

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.