Credit card associations, such as MasterCard, Visa and Bankcard, set interchange fees for transactions between members of the association. These same members often compete with each other on various aspects of a credit card transaction. The fact that interchange fees are the outcome of cooperation among firms that otherwise compete with each other, raises suspicion as to their efficiency and potential anti-competitive impact.
In the United States, several anti-trust actions have involved credit card interchange fees. For example, NaBanco, a potential merchant acquirer, brought an action against the Visa association alleging that the interchange fee was set prohibitively high. NaBanco claimed that the fee effectively excluded entrant acquirers and favoured incumbents who operated as both issuers and acquirers. That suit was dismissed but controversy remains with the U.S. Department of Justice, in 2000, initiating proceedings against MasterCard and Visa relating to dual membership by various banks in these associations.
Pension plans account for a large fraction of global institutional investment holdings. In 2008, $24.0 trillion of global institutional holdings was held by pension plans, making up 39% of the total. $15.3 trillion of these holdings were held in U.S. sponsored plans. In comparison with mutual and insurance funds, U.S. pension holdings comprise over 97% of the assets in these classes combined. Given the importance of pension funds as an investor class, it is surprising how little attention has been paid to unique features of pension funds in the academic literature.
We focus our analysis on privately sponsored U.S. defined benefit (DB) pension plans, a group with $1.9 trillion in assets as of the end of 2003 (see Buessing and Soto (2006)). Our analysis surrounds the determinants of decision-making in private DB pension plans. In doing so, we examine a number of related questions.
While the focus of biochemical research was addressed on the genome in the last decade the view is now turned onto the proteome. Big data sets of gene expression obtained from DNA-microarrays made the development of statistical methods necessary to make correct inferences from these measurements. For quantitative protein expression analysis either mass spectrometry (cf. Aebersold and Goodlett ([1]) and Gygi et al. ([7])) or two-dimensional gel electrophoresis (2-DE) (cf. Westermeier et al. ([14])) is applied. In this paper we focus on the analysis of protein expression data obtained from a new detection method (Difference GelElectrophoresis,DIGE) based on fluorescence labelling before 2-DE. 2-DE separates the proteins of a mixture by their isoelectric point (pI) and molecular size to distinct spots. After separation the proteins are detected using a confocal fluorescence scanner whereas fluorescence intensity of a spot can be regarded as a measure of expression for its respective protein. DIGE enables the user to put up to three different mixtures of proteins on the same gel.
The different mixtures are labelled by different fluorescence dyes (Cy2, Cy3 and Cy5). For quantitative proteome analysis image analysis software automatically determines the boundaries and sizes of the spots. Usually, a DIGE experiment is designed such that m independent replications of treatment and control mixtures are put on the same m gels. The internal standard, a mixture of same amounts of all m treatment and m control probes, is also put on each gel. This internal standard allows high accuracy calibration of the expression values. Calibration and normalization of protein expression data is reviewed in section 2. In order to obtain information about interactions of treatment and control with the time, DIGE experiments often include measurements over several time points. Known statistical methods for the analysis of longitudinal data can be used to analyze those experiments. One possible method for such an analysis is detailed in section 3. Often, 2-DE data contains up to 50% of missing values.
