Markttransparenz im CO<Subscript>2</Subscript>-Emissionshandel und Risikomanagement von Stromerzeugern

This paper analyses the effects of recent regulatory measures, namely the Regulation on wholesale Energy Market Integrity and Transparency enacted by the European Parliament and the Market Transparency Authority Act, that aim to increase transparency in CO₂ emissions trading on an utility maximizing electricity producer. Taking the particular characteristics of electricity into account, we analyze optimal composition of power generation and optimal risk policy. Our results have implications for the regulation of electricity producers with regard goals concerning environmental policy and supply reliability.     

Verteilungsaspekte einer Harmonisierung der europäischen Klimaschutzmaßnahmen

The ongoing harmonisation of the European energy sector has led to gains from growth and increasing efficiencies by creating a common European internal market. However, there are new tendencies running against harmonisation in recent years, currently for example in the area of climate protection and the associated expansion of renewable energies. In addition to the role of national targets for climate protection in combination with a European emissions trading system, carrying out the related measures leads to distributional effects, both between countries and between stakeholders (e. g., producers and consumers) in these countries. We first analyse the impact of a non-harmonised national promotion schemes for renewable energies on electricity and CO₂ prices in different countries. Then we illustrate the impact of harmonisation measures on producers and consumers in the participating countries. The analysis shows that these measures are indeed economically beneficial, but that—depending on the actual configuration—they can also lead to disadvantages for individual actors. Analysing and discussing these effects leads to a better assessment of the impact of certain measures and enables a better classification of individual market participants’ responses and opinions.     

Distribution of run statistics in partially exchangeable processes

Let { X_i} _{i 3 1}{{ X_{i}} _{igeq 1}} be an infinite sequence of recurrent partially exchangeable binary random variables. We study the exact distributions of two run statistics (total number of success runs and the longest success run) in { X_i} _{i 3 1}{{ X_{i}} _{igeq1}} . Since a flexible class of models for binary sequences can be obtained using the concept of partial exchangeability, as a special case of our results one can obtain the distribution of runs in ordinary Markov chains, exchangeable and independent sequences. The results also enable us to study the distribution of runs in particular urn models.     

Noncentral bimatrix variate generalised beta distributions

In this paper, we determine the density functions of nonsymmetrised doubly noncentral matrix variate beta type I and II distributions. The nonsymmetrised density functions of doubly noncentral and noncentral bimatrix variate generalised beta type I and II distributions are also obtained.     

Exact inference for progressively Type-I censored exponential failure data

For reasons of time constraint and cost reduction, censoring is commonly employed in practice, especially in reliability engineering. Among various censoring schemes, progressive Type-I right censoring provides not only the practical advantage of known termination time but also greater flexibility to the experimenter in the design stage by allowing for the removal of test units at non-terminal time points. In this article, we consider a progressively Type-I censored life-test under the assumption that the lifetime of each test unit is exponentially distributed. For small to moderate sample sizes, a practical modification is proposed to the censoring scheme in order to guarantee a feasible life-test under progressive Type-I censoring. Under this setup, we obtain the maximum likelihood estimator (MLE) of the unknown mean parameter and derive the exact sampling distribution of the MLE under the condition that its existence is ensured. Using the exact distribution of the MLE as well as its asymptotic distribution and the parametric bootstrap method, we then discuss the construction of confidence intervals for the mean parameter and their performance is assessed through Monte Carlo simulations. Finally, an example is presented in order to illustrate all the methods of inference discussed here.     

Optimality results on orthogonal arrays plus <Emphasis Type=Italic>p</Emphasis> runs for <Emphasis Type=Italic>s</Emphasis><Superscript><Emphasis Type=Italic>m</Emphasis></Superscript> factorial experiments

The optimality of designs obtained by adding p runs to an orthogonal array is studied for experiments involving m factors each at s levels. The optimality criterion used here, is the Type 1 criterion due to Cheng (1978) which is an extension of Kiefer (1975) universal optimality criterion. Unlike what happens with orthogonal array plus one run designs, the behavior of designs obtained via augmentation of an orthogonal array by p runs depends on the particular runs added.     

A MCGP decision aid for homebuyers to make the best choice

This study adopts a new approach, the multi-choice goal programming (MCGP), to evaluate houses in order to help homebuyers to find better house based on the residential preferences. According to the function of MCGP, homebuyers can set multiple housing goals with multiple aspiration levels. This increases the flexibility to find a suitable house. Compared with other classical methods such as checklist and analytic hierarchy process, MCGP is more efficient, especially while considering a lot of housing criteria and house alternatives. In order to demonstrate the usefulness of MCGP decision aid for housing selection, a real case study is then provided. Furthermore, ten volunteers are invited to participate in the empirical experiment. The results also validate the effectiveness and efficiency of MCGP decision aid.     

Monte Carlo theoretical trials of methods for assessing statistical significance for differences between adjusted odds ratios

Odds are generally defined as the number of successes divided by the number of failures in a given number of trials. An odds ratio is the ratio of one odds divided by another. Odds ratios can be adjusted to reflect associations with the outcome independently of the influence of associations with other variables. These are adjusted odds ratios. There are several well known methods for comparing odds ratios and testing for statistically significant differences between them. Analogous methods for adjusted odds ratios are not well known or well documented. One method for comparing adjusted odds ratios is explained by Hosmer and Lemeshow (Applied logistic regression, 2000). This method is used for the odds ratios for two variables from the same data set. The purpose of this analysis was to apply this method to a different situation: comparing odds ratios for the same variable from two different data sets. Monte Carlo trials were used to assess the performance of the method and these indicated the method performed well.