Power is one of the today’s major constraints for both hardware and software design. Thus the need to understand the statistics and distribution of power consumption from a hardware and software perspective is high. Power models satisfy this requirement to a certain extent, by estimating the power consumption for a subset of applications, or by providing a detailed power consumption distribution of a system. Till date, many power models have been proposed for the desktop and mobile platforms. However, most of these models were created based on power measurements performed on the entire system when different microbenchmarks stressing different blocks of the system were run. Then the measured power and the profiled information of the subsystem stressing benchmarks were used to create a regression analysis based model. Here, the power/energy prediction accuracy of the models created in this way, depend on both the method and accuracy of the power measurements and the type of regression used in generating the model. This work tries to eliminate the dependency of the accuracy of the power models on the type of regression analysis used, by performing power measurements at a subsystem granularity. When the power measurement of a single subsystem is obtained while stressing it, one can know the exact power it is consuming, instead of obtaining the power consumption of the entire system - without knowing the power consumption of the subsystem of interest - and depending on the regression analysis to provide the answer. Here we propose a generic method that can be used to create power models of individual subsystems of mobile platforms, and validate the method by presenting an empirical power model of the OMAP4460 based Pandaboard-ES, created using the proposed method. The created model has an average percentage of energy prediction error of just around -2.7% for the entire Pandaboard-ES system.

This document is a supplementary text to "An Empirical Power Analysis of Quasi-Exact Tests for the Rasch Model: Measurement Invariance in Small Samples" by Koller, Maier, & Hatzinger (to be published in Methodology, ISSN-L 1614-1881), which covers all technical details regarding the simulation and its results. First, the simulation scenarios and the introduction of differential item functioning (DIF) are described. Next, the different populations' distributions that were investigated are discussed, and finally, actual type-I-error rates and empirical power are displayed for all simulated scenarios. (authors' abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Neste trabalho, apresentamos alguns métodos para avaliação das previsões do Valor em Risco (VaR). Estes métodos testam um tipo de eficiência, denominada cobertura condicional correta. O poder empírico e a probabilidade do erro de tipo I são comparados através de simulações de Monte Carlo. Além disso, avaliamos um novo método de previsão do VaR, o qual é aplicado nos retornos diários do Ibovespa. Os resultados obtidos mostram que a nova classe de testes, baseados em uma regressão Weibull discreta, em muitos casos, tem poder empírico maior comparando com outros métodos apresentados neste trabalho. / In this paper, we present some procedures for assessing forecasts for the Value at Risk (VaR). These procedures test a type of efficiency, referred as correct conditional coverage. The empirical power and type I error probability are compared through a Monte Carlo simulation. The results show that a new class of tests based on a discrete Weibull regression in most cases has greater power empirical to other methods available in this paper.