Evaluating Statistical Methods Using Plasmode Data Sets in the Age of Massive Public Databases: An Illustration Using False Discovery Rates

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods

Red mud minimization by iron removal-iron reduction

A common feature of all red muds is the presence of substantial levels of iron oxide, present either as hematite or a hydrated ferric oxide. The iron oxide is the source of much of the cost of red mud treatment and disposal, and hinders the use of chemical processes for recovering the titania and other constituents. As a result, many proposed red mud treatment schemes feature a preliminary iron-removal step. An approach proposed in this research program is to reduce the ferric iron contents in red mud to magnetite, which is ferromagnetic and can be recovered by low-gradient magnetic separation. Magnetic separation of the magnetite will generate an iron-oxide concentrate which could be used by an iron-ore processor, leaving behind a residue smaller in volume and more easily processed for the recovery of more valuable constituents. This new process uses a direct reduction approach, mixing dried mud with several potential reductants, including coal, charcoal, sawdust and bagasse. Several variables which may have a potential impact on this new process such as the type of mud, the temperature, reduction time, and the mud/reducing agent ratio were investigated. The results indicate that the ferric iron in all three red muds is readily converted to magnetite, even at low temperatures. Sawdust and bagasse are more effective reducing agents than coal or charcoal, especially at lower temperatures. The conversion of ferric iron in red muds to magnetite is highly dependent on the temperature and mud/reducing agents ratio. However, the conversion quickly becomes invariant with time, suggesting that the reduction process might be controlled by the surface reaction --Abstract, page iii

Parameter estimation for a finite mixture model in high dimensional applications

Finite mixture models have found use in the analysis of high dimensional data such as result from microarray experiments. A common goal of a microarray experiment is to identify genes that express differentially between two types of tissues or between two experimental conditions. Some investigators found that the distribution of P-values from tests for differential genetic expression contains useful information regarding several quantities of interest. A uniform-beta mixture distribution (mix-o-matic) has been employed to model this distribution...This dissertation covers three topics: 1) the performance of interval estimates of model parameters using three computational methods including a comparison of the computational methods; 2: a relatively recent approach based on a number theoretic method for obtaining MLEs, its extensions and a comparison to Newton-type methods; 3) FDR estimation in the mix-o-matic and a comparison with eight other techniques for estimating FDR, all techniques making use of information in the distribution of P-values --Abstract, page iii

Enthalpies of Mixing in Fe-C-Si Melts

The enthalpy of mixing of molten Fe-C-Si alloys is a significant element in performing mass balances on iron production and steelmaking, but almost no experimental data has been reported on enthalpy determination in these melts. As a result, models expressing this property as a function of composition must be derived from measurements of component activity, which are more widely available. The use of three such models - the regular solution model, Wagner\u27s model for dilute solutions, and the unified interaction parameter model developed by Bale and Pelton - have been used to calculate enthalpies of mixing in Fe-C-Si melts. The results are compared with each other and with the experimental data of Vitusevich et al. © 2001 Elsevier Science B.V

RockSee: Video Image Measurements of Physical Features to Aid in Highway Rock Cut Characterization

Maintaining highway rock cuts for the safety of the motoring public from the risk and consequence of falling rock is an enormous task for State Departments of Transportation. The amount of work to even evaluate and prioritize the remediation effort is prohibitive. To facilitate the prioritization of remediation efforts, a new rock fall hazard rating system has been developed for Missouri highways. To make the process much more efficient, video logs are used screen the rock cuts, parameters used in the system are measured on video images, and data is automatically transferred to a GIS system

Digital Imaging for Screening and Making Measurement of Features on Highway Rock Cuts

Highway rock cuts must be maintained for the safety of the motoring public. Since highways cover vast areas through differing geological terrains, it is not cost effective to remediate all rock cuts; remediation efforts have to be prioritized. Even doing traditional geological engineering evaluations on all the rock cuts is prohibitive. Most jurisdictions now use a rock mass classification such as the Oregon Rock Hazard Rating System (RHRS) to streamline the process by quickly classifying rock cuts, rather than evaluating each in detail. The cuts that have the worst score in the classification can then be further evaluated in the traditional way. This paper demonstrates how further efficiencies can be realized, by using computer scaled video images. Digital video image of entire highways can be acquired at highway speeds. Later using a computer, engineers can review the video, select areas that look like they may be problematic, and plan further investigations at those sites. Additionally, some of the parameters required in the classification systems, such as slope heights and slope angles can be measured directly on the digital images. A low cost, state-of-the-art system developed to perform these tasks is described here. Typical measurement can be made with errors of less than 10%, which is more than adequate for the purposes of rock mass classification, and estimating rock quantities

Interval Estimation in a Finite Mixture Model: Modeling P-values in Multiple Testing Applications

The performance of interval estimates in a uniform-beta mixture model is evaluated using three computational strategies. Such a model has found use when modeling a distribution of P-values from multiple testing applications. The number of P-values and the closeness of a parameter to the boundary of its space both play a role in the precision of parameter estimates as does the “nearness” of the beta-distribution component to the uniform distribution. Three computational strategies are compared for computing interval estimates with each one having advantages and disadvantages for cases considered here

Low-temperature Reduction of Ferric Iron in Red Mud

Previous proposed methods for removing the iron from red mud have focused on either DRI-type processing or blast furnace smelting. A new iron-removal process features low-temperature reduction of ferric iron content to magnetite, followed by magnetic separation. The results of reduction experiments using coal, charcoal, sawdust and bagasse as solid-state reducing agents are described. Other variables included the type of mud used (three U.S. producers), reduction time and temperature, and the mud/reductant mass ratio. Sawdust and bagasse are the better reductants, and complete reduction to magnetite can be achieved at temperatures as low as 350°C. Conversion of the ferric iron to magnetite is strongly dependent on the mud/reductant ratio, suggesting that pyrolysis of the reductant is the controlling factor in the reduction process. Initial experiments on recovery of the magnetite generated by the reduction process will be described