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The main focus of this thesis is to develop full parametric and semiparametric Bayesian inference for data arising from triangular distributions. A natural consequence of working with such distributions is it allows one to consider regression models where the response variable is now the mode of the data distribution. A new family of nonparametric prior distributions is developed for a certain class of convex densities of particular relevance to mode regressions. Triangular distributions arise in several contexts such as geosciences, econometrics, finance, health care management, sociology, reliability engineering, decision and risk analysis, etc. In many fields, experts, typically, have a reasonable idea about the range and most likely values that define a data distribution. Eliciting these quantities is thus, generally, easier than eliciting moments of other commonly known distributions. Using simulated and actual data, applications of triangular distributions, with and without mode regressions, in some of the aforementioned areas are tackled. / text

Ling, Chi Yeung = 作高能量密度物理研究的一種新型強離子束加速器的模擬 / 凌子陽. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 111-114). / Abstracts in English and Chinese. / Ling, Chi Yeung = Zuo gao neng liang mi du wu li yan jiu de yi zhong xin xing qiang li zi shu jia su qi de mo ni / Ling Ziyang. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- High Energy Density Physics and Warm Dense Matter --- p.7 / Chapter 2.1.1 --- Definition of HEDP and WDM --- p.7 / Chapter 2.1.2 --- The physics of WDM --- p.9 / Chapter 2.1.3 --- Advantages of the ion beam approach --- p.10 / Chapter 2.2 --- Intense low energy ion beam machines requirements for NDCX-II --- p.12 / Chapter 2.3 --- Neutralized Drift Compression Experiment (NDCX) --- p.14 / Chapter 2.3.1 --- Neutralized Transport Experiment (NTX) --- p.15 / Chapter 2.3.2 --- The first NDCX --- p.18 / Chapter 2.4 --- Accelerator architectures proposed for NDCX-II --- p.20 / Chapter 2.4.1 --- Radio Frequency Linear Accelerator (RF Linac) --- p.20 / Chapter 2.4.2 --- Electrostatic accelerator --- p.23 / Chapter 2.4.3 --- Drift Tube Linac (DTL) --- p.23 / Chapter 2.4.4 --- Linear Induction Accelerator (induction linac) --- p.24 / Chapter 2.5 --- Pulse Line Ion Accelerator --- p.25 / Chapter 2.6 --- Review on tests of Pulse Line Ion Accelerator --- p.30 / Chapter 2.7 --- Simulation codes --- p.32 / Chapter 2.7.1 --- 3-D Electromagnetic code MAFIA --- p.33 / Chapter 2.7.2 --- Particle-in-cell code WARP --- p.35 / Chapter 2.8 --- Envelope equation of ion beam and beam diagnostics --- p.37 / Chapter 3 --- Investigations on insulator breakdown in the PLIA --- p.40 / Chapter 3.1 --- Modeling in MAFIA --- p.40 / Chapter 3.2 --- Scaling Law --- p.42 / Chapter 3.3 --- Investigation of different frequency modes near insulator surface --- p.46 / Chapter 3.4 --- Standing wave effect in PLIA --- p.50 / Chapter 3.5 --- Conclusion --- p.52 / Chapter 4 --- PLIA based design for the second Neutralized Drift Compression Experiment --- p.55 / Chapter 4.1 --- The injector --- p.56 / Chapter 4.2 --- Pulse Line Ion Accelerator sections --- p.60 / Chapter 4.2.1 --- Basic design strategy --- p.60 / Chapter 4.2.2 --- Simulation results of PLIA sections --- p.69 / Chapter 4.3 --- Neutralized Drift Compression Section --- p.77 / Chapter 4.3.1 --- Drift length --- p.78 / Chapter 4.3.2 --- First focusing solenoid --- p.80 / Chapter 4.3.3 --- Plasma-filled region --- p.84 / Chapter 4.3.4 --- Final focusing solenoid and the best focal point --- p.88 / Chapter 4.3.5 --- Sensitivity to drift length and focusing strength --- p.91 / Chapter 4.4 --- Conclusion --- p.92 / Chapter 5 --- Other Pulse Power Options --- p.94 / Chapter 5.1 --- The injector and the beamline --- p.95 / Chapter 5.2 --- 3-meter electrostatic column --- p.97 / Chapter 5.3 --- Induction linac --- p.100 / Chapter 5.4 --- Hybrid of induction linac and Pulse Line Ion Accelerator --- p.104 / Chapter 5.5 --- Conclusion --- p.107 / Chapter 6 --- Discussions --- p.108 / Chapter 6.0.1 --- Future development of PLIA --- p.110 / Bibliography --- p.111

In financial econometrics the modeling of asset return series is closely related to the estimation of the corresponding conditional densities. One reason why one is interested in the whole conditional density and not only in the conditional mean, is that the conditional variance can be interpreted as a measure of time-dependent volatility of the return series. In fact, the modeling and the prediction of volatility is one of the central topics in asset pricing. In this paper we propose to estimate conditional densities semi-nonparametrically in a neural network framework. Our recurrent mixture density networks realize the basic ideas of prominent GARCH approaches but they are capable of modeling any continuous conditional density also allowing for time-dependent higher-order moments. Our empirical analysis on daily DAX data shows that out-of-sample volatility predictions of the neural network model are superior to predictions of GARCH models in that they have a higher correlation with implied volatilities. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Okra (Abelmoschus esculentus) is a traditional crop commercially cultivated in many parts of the world. Fresh okra has a high nutritional value and grows very quickly with high temperatures, which lends its production to more tropical areas. This study was implemented to evaluate different varieties of okra and determine the optimum density for production in Massachusetts. Two experiments were carried out between May and September of 2009 and 2010 at the UMass Research Farm in South Deerfield, MA. For the variety trial in 2009: Annie Oakley, Baby Bubba, Cajun Delight, Chifre de Veado, Clemson Spineless, Millionaire, North & South and Santa Cruz 47. The immature pods were harvested when reached 70 mm in length (size desired by the market in the USA) and in another plot for Chifre de Veado and Santa Cruz 47 the pods were harvested when reached 100 mm (market in Brazil). The density trial was set in a randomized complete block design with seven different plant spacings (7.5, 15.0, 22.5, 30.0, 38.5, 45.0 and 52.5 cm) in double row of plants of Cajun Delight. The pods were harvested three times a week, counted and weighted. Analyses of variance were performed by SAS, and means were compared using Duncan’s new multiple range test (P = 0.05) and orthogonal polynomial comparisons. In 2010, Santa Cruz 47 harvested based on Brazilian market size had the best performance over the season with the yield of 17.86 ton.ha-1 and similar statistic results comparing to North and South (15.99 ton.ha-1) and Annie Oakley (15.24 ton.ha-1). The differences among the plant spacings in 2010, were represented by a quadratic relationship, where the greater plant spacing for yield was ‘52.5 cm’ with the total yield of 14.91 ton.ha-1. Both trials in 2009 were negatively affected by the soil-borne fungus Verticillium spp., which, combined with the cold and wet weather, became very aggressive, especially in the end of the season. The results show that the varieties: North and South, Annie Oakley, Cajun Delight, Millionaire, Clemson Spineless, Santa Cruz 47 can be commercially grown in Massachusetts and the recommended plant spacing of okra is 52.5 cm.

<p> High resolution lithium-drifted Germanium detectors have been used to study the γ radiation emitted after thermal neutron capture in ten odd-proton even-neutron nuclei in the mass range 56 ≤ A ≤ 204. From the energies and intensities of these radiations, the energy dependence of the partial radiative widths to bound states with excitation energies in the region 0 to 2.5 MeV have been deduced. The energy dependence for the average spacing between the levels populated in this reaction have also been deduced over the same region of excitation energies. The energy dependence of the partial radiative widths are in agreement with the single particle estimate for dipole radiation and the energy dependence of the average level spacing favours the constant temperature model.</p> / Thesis / Doctor of Philosophy (PhD)

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Parmi les outils les plus classiques de l'Analyse Multivariée, les Composantes Principales sont aussi un des plus anciens puisqu'elles furent introduites il y a plus d'un siècle par Pearson (1901) et redécouvertes ensuite par Hotelling (1933). Aujourd'hui, cette méthode est abondamment utilisée en Sciences Sociales, en Economie, en Biologie et en Géographie pour ne citer que quelques disciplines. Elle a pour but de réduire de façon optimale (dans un certain sens) le nombre de variables contenues dans un jeu de données. A ce jour, les méthodes d'inférence utilisées en Analyse en Composantes Principales par les praticiens sont généralement fondées sur l'hypothèse de normalité des observations. Hypothèse qui peut, dans bien des situations, être remise en question. Le but de ce travail est de construire des procédures de test pour l'Analyse en Composantes Principales qui soient valides sous une famille plus importante de lois de probabilité, la famille des lois elliptiques. Pour ce faire, nous utilisons la méthodologie de Le Cam combinée au principe d'invariance. Ce dernier stipule que si une hypothèse nulle reste invariante sous l'action d'un groupe de transformations, alors, il faut se restreindre à des statistiques de test également invariantes sous l'action de ce groupe. Toutes les hypothèses nulles associées aux problèmes considérés dans ce travail sont invariantes sous l'action d'un groupe de transformations appellées monotones radiales. L'invariant maximal associé à ce groupe est le vecteur des signes multivariés et des rangs des distances de Mahalanobis entre les observations et l'origine. Les paramètres d'intérêt en Analyse en composantes Principales sont les vecteurs propres et valeurs propres de matrices définies positives. Ce qui implique que l'espace des paramètres n'est pas linéaire. Nous développons donc une manière d'obtenir des procédures optimales pour des suite d'experiences locales courbées. Les statistiques de test introduites sont optimales au sens de Le Cam et mesurables en l'invariant maximal décrit ci-dessus. Les procédures de test basées sur ces statistiques possèdent de nombreuses propriétés attractives: elles sont valides sous la famille des lois elliptiques, elles sont efficaces sous une densité spécifiée et possèdent de très bonnes efficacités asymptotiques relatives par rapport à leurs concurrentes. En particulier, lorsqu'elles sont basées sur des scores Gaussiens, elles sont aussi efficaces que les procédures Gaussiennes habituelles et sont bien plus efficaces que ces dernières si l'hypothèse de normalité des observations n'est pas remplie.

State price density (SPD) and stochastic discount factor (SDF) are important elements in asset pricing. In this thesis, I first propose to use projection pursuit regression (PPR) and local polynomial regression (LPR) to estimate the SPD of interest rates nonparametrically. By using a similar approach, I also estimate the delta values in the interest rate options and discusses how to delta-hedge these options. Unlike SPD measured in a risk-neutral economy, SDF is implied by an asset pricing model. It displays which prices are reasonable given the returns in the current period. Hansen and Jagannathan (1997) develop the Hansen-Jagannathan distance (HJ-distance) to measure pricing errors produced by SDF. While the HJ-distance has several desirable properties, Ahn and Gadarowski (2004) find that the specification test based on the HJ-distance overrejects correct models too severely in commonly used sample size to provide a valid test. This thesis proposes to improve the finite sample properties of the HJ-distance test by applying the shrinkage method (Ledoit and Wolf, 2003) to compute its weighting matrix. / Thesis (Ph.D, Economics) -- Queen's University, 2008-06-19 00:00:55.996

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