Thesis (Ph. D.--Music)--University of California, San Diego, 2006. / First work for 2 flutes, 2 clarinets, violoncello, and piano; 2nd for oboe and violin solo with 2 trumpets, horn, trombone, crotales, vibraphone, and percussion. Vita. With sound recording (1 sound disc : digital ; 4 3/4 in.) containing thesis works.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1974. / Typescript and manuscript. Vita. For 2 choruses (SATB), 2 speakers, and instrumental ensemble (piano, harp, percussion, flute, oboe, bass clarinet, trumpet, horn, bass trombone, 2 violins, 2 violoncellos, and 2 double basses); performance also requires people to control slide projections and lighting changes. English words. Text for score is from Credo one and Credo two, by Gavin Arthur; text read by speakers before the beginning of the piece is by Jalal ud-din Rumi, a 13th-cent. mystical Persian poet. Includes performance instructions. Includes the compositions "Winter's not forever (on three poems by e. e. cummings) for soprano, female speaker, and six players" (leaves 124-148) and "--And it always will be (for percussion soloist with orchestra)" (leaves 149-186) by the author. "Winter's not forever": for soprano, speaker, flute, horn, bass clarinet, double bass, percussion, and piano/celeste. Description based on print version record.

Thesis (M.Phil.) - University of Queensland, 2002. / Includes bibliography.

Thesis (M.S.)--University of Wisconsin--Madison, 1968. / For unison to 4-part children's chorus (specifically, 5th graders) with piano (in part also with flute or percussion). Ms. (arranger's holograph). Includes bibliographical references. eContent provider-neutral record in process. Description based on print version record.

Dissertation (D.M.A.)--The University of North Carolina at Greensboro, 2010. / Directed by John Locke; submitted to the School of Music. Title from PDF t.p. (viewed Jul. 7, 2010). Includes bibliographical references (p. 43-45).

Thesis (Ph. D.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (leaves 207-216). Also available via the World Wide Web.

Thesis (M.M.) Florida State University, 2004. / Advisor: Ladislav Kubik, Florida State University, College of Music. Title and description from dissertation home page (viewed 8-20-2007). Document formatted into pages; contains 47 pages. Includes biographical sketch.

Until recent times, weather forecasts were deterministic in nature. For example, a forecast might state ``The temperature tomorrow will be $20^\circ$C.'' More recently, however, increasing interest has been paid to the uncertainty associated with such predictions. By quantifying the uncertainty of a forecast, for example with a probability distribution, users can make risk-based decisions. The uncertainty in weather forecasts is typically based upon `ensemble forecasts'. Rather than issuing a single forecast from a numerical weather prediction (NWP) model, ensemble forecasts comprise multiple model runs that differ in either the model physics or initial conditions. Ideally, ensemble forecasts would provide a representative sample of the possible outcomes of the verifying observations. However, due to model biases and inadequate specification of initial conditions, ensemble forecasts are often biased and underdispersed. As a result, estimates of the most likely values of the verifying observations, and the associated forecast uncertainty, are often inaccurate. It is therefore necessary to correct, or post-process ensemble forecasts, using statistical models known as `ensemble post-processing methods'. To this end, this thesis is concerned with the application of statistical methodology in the field of probabilistic weather forecasting, and in particular ensemble post-processing. Using various datasets, we extend existing work and propose the novel use of statistical methodology to tackle several aspects of ensemble post-processing. Our novel contributions to the field are the following. In chapter~3 we present a comparison study for several post-processing methods, with a focus on probabilistic forecasts for extreme events. We find that the benefits of ensemble post-processing are larger for forecasts of extreme events, compared with forecasts of common events. We show that allowing flexible corrections to the biases in ensemble location is important for the forecasting of extreme events. In chapter~4 we tackle the complicated problem of post-processing ensemble forecasts without making distributional assumptions, to produce recalibrated ensemble forecasts without the intermediate step of specifying a probability forecast distribution. We propose a latent variable model, and make a novel application of measurement error models. We show in three case studies that our distribution-free method is competitive with a popular alternative that makes distributional assumptions. We suggest that our distribution-free method could serve as a useful baseline on which forecasters should seek to improve. In chapter~5 we address the subject of parameter uncertainty in ensemble post-processing. As in all parametric statistical models, the parameter estimates are subject to uncertainty. We approximate the distribution of model parameters by bootstrap resampling, and demonstrate improvements in forecast skill by incorporating this additional source of uncertainty in to out-of-sample probability forecasts. In chapter~6 we use model diagnostic tools to determine how specific post-processing models may be improved. We subsequently introduce bias correction schemes that move beyond the standard linear schemes employed in the literature and in practice, particularly in the case of correcting ensemble underdispersion. Finally, we illustrate the complicated problem of assessing the skill of ensemble forecasts whose members are dependent, or correlated. We show that dependent ensemble members can result in surprising conclusions when employing standard measures of forecast skill.

Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for optimizing RF further by enhancing and improving its performance accuracy. This explains why there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. The main focus of this dissertation is to develop new extensions of RF using new optimization techniques that, to the best of our knowledge, have never been used before to optimize RF. These techniques are clustering, the local outlier factor, diversified weighted subspaces, and replicator dynamics. Applying these techniques on RF produced four extensions which we have termed CLUB-DRF, LOFB-DRF, DSB-RF, and RDB-DR respectively. Experimental studies on 15 real datasets showed favorable results, demonstrating the potential of the proposed methods. Performance-wise, CLUB-DRF is ranked first in terms of accuracy and classifcation speed making it ideal for real-time applications, and for machines/devices with limited memory and processing power.

abstract: A new arrangement of the Concerto for Two Horns in E-flat Major, Hob. VIId/6, attributed by some to Franz Joseph Haydn, is presented here. The arrangement reduces the orchestral portion to ten wind instruments, specifically a double wind quintet, to facilitate performance of the work. A full score and a complete set of parts are included. In support of this new arrangement, a discussion of the early treatment of horns in pairs and the subsequent development of the double horn concerto in the eighteenth century provides historical context for the Concerto for Two Horns in E-flat major. A summary of the controversy concerning the identity of the composer of this concerto is followed by a description of the content and structure of each of its three movements. Some comments on the procedures of the arrangement complete the background information. / Dissertation/Thesis / D.M.A. Music 2011