Audit of the Parking System Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Parking System Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Ice Arena Facility Revenue Note Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Ice Arena Facility Revenue Note Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Regulated Materials Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Regulated Materials Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Memorial Union Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Memorial Union Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Annual Report of the Iowa Department of Revenue FY2007

Annual Report of the Iowa Department of Revenue FY2007

Addendum to Annual Report of the Iowa Department of Revenue FY2007

Addendum to Annual Report of the Iowa Department of Revenue FY2007. Comparison to prior years. Local option tax distributions.

On some geometry and equivalence classes of normal form games

Equivalence classes of normal form games are defined using the geometryof correspondences of standard equilibiurm concepts like correlated, Nash,and robust equilibrium or risk dominance and rationalizability. Resultingequivalence classes are fully characterized and compared across differentequilibrium concepts for 2 x 2 games. It is argued that the procedure canlead to broad and game-theoretically meaningful distinctions of games aswell as to alternative ways of viewing and testing equilibrium concepts.Larger games are also briefly considered.

Revenue management under general discrete choice model of consumer behavior

Customer choice behavior, such as 'buy-up' and 'buy-down', is an importantphe-nomenon in a wide range of industries. Yet there are few models ormethodologies available to exploit this phenomenon within yield managementsystems. We make some progress on filling this void. Specifically, wedevelop a model of yield management in which the buyers' behavior ismodeled explicitly using a multi-nomial logit model of demand. Thecontrol problem is to decide which subset of fare classes to offer ateach point in time. The set of open fare classes then affects the purchaseprobabilities for each class. We formulate a dynamic program todetermine the optimal control policy and show that it reduces to a dynamicnested allocation policy. Thus, the optimal choice-based policy caneasily be implemented in reservation systems that use nested allocationcontrols. We also develop an estimation procedure for our model based onthe expectation-maximization (EM) method that jointly estimates arrivalrates and choice model parameters when no-purchase outcomes areunobservable. Numerical results show that this combined optimization-estimation approach may significantly improve revenue performancerelative to traditional leg-based models that do not account for choicebehavior.

Distributional equivalence and subcompositional coherence in the analysis of contingency tables, ratio-scale measurements and compositional data

We consider two fundamental properties in the analysis of two-way tables of positive data: the principle of distributional equivalence, one of the cornerstones of correspondence analysis of contingency tables, and the principle of subcompositional coherence, which forms the basis of compositional data analysis. For an analysis to be subcompositionally coherent, it suffices to analyse the ratios of the data values. The usual approach to dimension reduction in compositional data analysis is to perform principal component analysis on the logarithms of ratios, but this method does not obey the principle of distributional equivalence. We show that by introducing weights for the rows and columns, the method achieves this desirable property. This weighted log-ratio analysis is theoretically equivalent to spectral mapping , a multivariate method developed almost 30 years ago for displaying ratio-scale data from biological activity spectra. The close relationship between spectral mapping and correspondence analysis is also explained, as well as their connection with association modelling. The weighted log-ratio methodology is applied here to frequency data in linguistics and to chemical compositional data in archaeology.

Proving the performance of a new revenue management system

Revenue management (RM) is a complicated business process that can best be described ascontrol of sales (using prices, restrictions, or capacity), usually using software as a tool to aiddecisions. RM software can play a mere informative role, supplying analysts with formatted andsummarized data who use it to make control decisions (setting a price or allocating capacity fora price point), or, play a deeper role, automating the decisions process completely, at the otherextreme. The RM models and algorithms in the academic literature by and large concentrateon the latter, completely automated, level of functionality.A firm considering using a new RM model or RM system needs to evaluate its performance.Academic papers justify the performance of their models using simulations, where customerbooking requests are simulated according to some process and model, and the revenue perfor-mance of the algorithm compared to an alternate set of algorithms. Such simulations, whilean accepted part of the academic literature, and indeed providing research insight, often lackcredibility with management. Even methodologically, they are usually awed, as the simula-tions only test \within-model" performance, and say nothing as to the appropriateness of themodel in the first place. Even simulations that test against alternate models or competition arelimited by their inherent necessity on fixing some model as the universe for their testing. Theseproblems are exacerbated with RM models that attempt to model customer purchase behav-ior or competition, as the right models for competitive actions or customer purchases remainsomewhat of a mystery, or at least with no consensus on their validity.How then to validate a model? Putting it another way, we want to show that a particularmodel or algorithm is the cause of a certain improvement to the RM process compared to theexisting process. We take care to emphasize that we want to prove the said model as the causeof performance, and to compare against a (incumbent) process rather than against an alternatemodel.In this paper we describe a \live" testing experiment that we conducted at Iberia Airlineson a set of flights. A set of competing algorithms control a set of flights during adjacentweeks, and their behavior and results are observed over a relatively long period of time (9months). In parallel, a group of control flights were managed using the traditional mix of manualand algorithmic control (incumbent system). Such \sandbox" testing, while common at manylarge internet search and e-commerce companies is relatively rare in the revenue managementarea. Sandbox testing has an undisputable model of customer behavior but the experimentaldesign and analysis of results is less clear. In this paper we describe the philosophy behind theexperiment, the organizational challenges, the design and setup of the experiment, and outlinethe analysis of the results. This paper is a complement to a (more technical) related paper thatdescribes the econometrics and statistical analysis of the results.

Report of Recommendations to the Iowa Department of Revenue for the year ended June 30, 2007

Report on the Iowa Department of Revenue for the year ended June 30, 2007

Report of the Academic Building Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Academic Building Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Dormitory and Dining Services Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Dormitory and Dining Services Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Recreational Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Recreational Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008