本研究旨在探討我國生醫產業公開說明書之資訊揭露程度對初次公開發行 (IPO, initial public offering)定價效率及次級市場投資人信念異質性程度之影響。當初級市場認購人間資訊不對稱程度越大時，IPO價值之事前不確定性越高。為均衡各交易參與者之利益，發行人與承銷商將主動提升公開說明書之資訊揭露程度，以制定適當的IPO折價幅度、維持承銷商合理的承銷風險與報酬，並協助認購人適切評定IPO之價值。異質信念觀點強調次級市場投資人對企業價值看法之歧異程度越大，將導致IPO蜜月期報酬之異常現象。本研究預期若無形資產密集度越高，IPO事前不確定性越大，則IPO折價幅度越大，並預期公開說明書之資訊揭露程度將改變無形資產密集度對折價幅度之影響程度。此外，本研究預期，生醫產業IPO案件之無形資產密集度、公開說明書之資訊揭露程度及者配售情形均可能影響投資人信念異質性，進而影響掛牌初期之成交價量表現。 本研究參考國外證券主管機關之無形資產資訊揭露規範，自行建立資訊揭露指標，系統性地衡量我國生醫產業公開說明書之資訊揭露程度，並以多元迴歸分析檢測假說。實證結果顯示，我國生醫產業IPO案件之無形資產密集度對IPO折價幅度存在顯著正向影響；公開說明書之資訊揭露程度改變無形資產密集度對折價幅度之影響程度；發行人之無形資產密集度、公開說明書資訊揭露程度及初級市場配售情形皆影響掛牌初期之投資人信念異質性及價格震盪幅度。研究結果證實公開說明書之資訊揭露提供預期效益，及初級與次級市場間之資訊相互流通、交易行為相互連動之關聯性。 / This research examines how the disclosure level of prospectus influences the efficiency of IPO (initial public offering) pricing in the primary market and the degree of divergence of opinions in the secondary market. The literature of IPO underpricing suggests that ex ante uncertainty due to information asymmetry has a positive impact on IPO discount and voluntary disclosure of prospectuses may reduce the uncertainty level. This research hypothesizes that, for biotechnology companies, a greater disclosure level of prospectuses would lower the impact of intensity of intangibles on IPO discount. Further, this research hypothesizes a relation between the pricing efficiency in primary market and the level of divergence of opinions in secondary market. The empirical results from regression analyses of hand-collected data show that, for biotechnology IPOs, the disclosure level of prospectuses reduces the impact of the intensity of intangibles on IPO discount. In addition, the intensity of intangibles, disclosure level of prospectuses, and trading behaviors in the primary market have an effect on the degree of divergence of opinions in the secondary market. In sum, this research evidences the expected benefits of the increased level of voluntary disclosure of prospectuses for biotechnology IPOs.

Bragg scattering of optical waves in thick gratings at extreme angles, where the scattered wave propagates parallel (extremely asymmetric scattering - EAS) or nearly parallel (grazing angle scattering - GAS) to the grating boundaries, is associated with many unique and practically important resonant phenomena. It has been demonstrated that one of the main physical mechanisms for these resonant phenomena is the diffractional divergence of the scattered wave inside and outside the grating region. This thesis fills the gaps in the theoretical and experimental understanding of Bragg scattering in gratings at extreme angles by investigating EAS and GAS in structures where diffractional divergence of waves is significantly affected by anisotropy and/or non-uniformities of the dielectric permittivity. Unusually high sensitivity of wave scattering in thick periodic gratings to small step-like variations of mean structural parameters at the grating boundaries is predicted and described for the case when the scattered wave (the +1 diffracted order) propagates almost parallel to the front grating boundary (the geometry of GAS). A unusual pattern of strong multiple resonances for bulk electromagnetic waves is predicted and analysed numerically in thick periodic holographic gratings in a guiding slab with mean permittivity that is greater than that of the surrounding media. It is demonstrated that these resonances are related to resonant generation of a new type of eigenmodes in a thick slab with a periodic grating. These eigenmodes are generically related to the grating -- they do exist not if the grating amplitude is zero. A new type of resonant coupling of bulk radiation into the conventional guided modes of a slab with a thick holographic grating is predicted and explained theoretically. It occurs in the presence of strong frequency detunings of the Bragg condition by means of interaction of the strongly non-eigen +1 diffracted order with the slab-grating boundaries. Therefore, it is only in the presence of step-like variations of the mean permittivity at the grating boundaries that this type of resonant coupling can occur. A new method for the analysis of EAS and GAS in anisotropic gratings is developed. This method is based on the consideration of the diffractional divergence of the scattered wave and the two-wave approximation in anisotropic gratings. Special efforts are focused on the analysis of EAS and GAS of extraordinary waves in uniaxial gratings. In particular, it is demonstrated that increasing curvature of the normal surface in the direction of propagation of the scattered wave results in increase of its diffraction divergence and the resonant amplitude. A theoretical model is developed for comparison of the theoretical predictions with data obtained from experimental observations of EAS in a holographic grating written in a photorefractive medium. The developed model is applied for the interpretation of experimental observations of EAS in BaTiO3 photorefractive crystals. Good agreement with the theoretical predictions is demonstrated.

The purpose of this study is to analyse the level of good governance understanding implementation in Indonesia regional governments, identify impeding variables to good governance implementation, and evaluate the extent of international good governance standards alignment. The influence of economic and political transition, decentralisation and regional autonomy regime, bureaucracy culture, and political history is analysed to reflect the degree of good governance implementation and level of convergence to international good governance standards. The methodological approach involves a triangulation of in-depth interview, document analysis, and International Good Governance Standard comparison. Findings from the study reflect disparities in good governance understanding and implementation between Indonesia regional governments, nine main impeding variables to good governance implementation including bureaucratic culture and political history, and a positive response to convergence towards international good governance standard alignment. Findings also act as an in depth study and analysis of current Indonesia regional government situation, resulting in inputs and recommendations geared towards public policy development and good governance implementation guidelines.

Made available in DSpace on 2016-06-02T20:04:53Z (GMT). No. of bitstreams: 1 6424.pdf: 5426262 bytes, checksum: 82bb9386f85845b0d3db787265ea8236 (MD5) Previous issue date: 2014-11-21 / The Generalized Normal (GN) and Generalized lognormal (logGN) distributions are flexible for accommodating features present in the data that are not captured by traditional distribution, such as the normal and the lognormal ones, respectively. These distributions are considered to be tools for the reduction of outliers and for the obtention of robust estimates. However, computational problems have always been the major obstacle to obtain the effective use of these distributions. This paper proposes the Bayesian reference analysis methodology to estimate the GN and logGN. The reference prior for a possible order of the model parameters is obtained. It is shown that the reference prior leads to a proper posterior distribution for all the proposed model. The development of Monte Carlo Markov Chain (MCMC) is considered for inference purposes. To detect possible influential observations in the models considered, the Bayesian method of influence analysis on a case based on the Kullback-Leibler divergence is used. In addition, a scale mixture of uniform representation of the GN and logGN distributions are exploited, as an alternative method in order, to allow the development of efficient Gibbs sampling algorithms. Simulation studies were performed to analyze the frequentist properties of the estimation procedures. Real data applications demonstrate the use of the proposed models. / As distribuições normal generalizada (NG) e lognormal generalizada (logNG) são flexíveis por acomodarem características presentes nos dados que não são capturadas por distribuições tradicionais, como a normal e a lognormal, respectivamente. Essas distribuições são consideradas ferramentas para reduzir as observações aberrantes e obter estimativas robustas. Entretanto o maior obstáculo para a utilização eficiente dessas distribuições tem sido os problemas computacionais. Este trabalho propõe a metodologia da análise de referência Bayesiana para estimar os parâmetros dos modelos NG e logNG. A função a priori de referência para uma possível ordem dos parâmetros do modelo é obtida. Mostra-se que a função a priori de referência conduz a uma distribuição a posteriori própria, em todos os modelos propostos. Para fins de inferência, é considerado o desenvolvimento de métodos Monte Carlo em Cadeias de Markov (MCMC). Para detectar possíveis observações influentes nos modelos considerados, é utilizado o método Bayesiano de análise de influência caso a caso, baseado na divergência de Kullback-Leibler. Além disso, uma representação de mistura de escala uniforme para as distribuições NG e logNG é utilizada, como um método alternativo, para permitir o desenvolvimento de algoritmos de amostrador de Gibbs. Estudos de simulação foram desenvolvidos para analisar as propriedades frequentistas dos processos de estimação. Aplicações a conjuntos de dados reais mostraram a aplicabilidade dos modelos propostos.

We study minimization problems with respect to a one-parameter family of generalized relative entropies. These relative entropies, which we call relative -entropies (denoted I (P; Q)), arise as redundancies under mismatched compression when cumulants of compression lengths are considered instead of expected compression lengths. These parametric relative entropies are a generalization of the usual relative entropy (Kullback-Leibler divergence). Just like relative entropy, these relative -entropies behave like squared Euclidean distance and satisfy the Pythagorean property. We explore the geometry underlying various statistical models and its relevance to information theory and to robust statistics. The thesis consists of three parts. In the first part, we study minimization of I (P; Q) as the first argument varies over a convex set E of probability distributions. We show the existence of a unique minimizer when the set E is closed in an appropriate topology. We then study minimization of I on a particular convex set, a linear family, which is one that arises from linear statistical constraints. This minimization problem generalizes the maximum Renyi or Tsallis entropy principle of statistical physics. The structure of the minimizing probability distribution naturally suggests a statistical model of power-law probability distributions, which we call an -power-law family. Such a family is analogous to the exponential family that arises when relative entropy is minimized subject to the same linear statistical constraints. In the second part, we study minimization of I (P; Q) over the second argument. This minimization is generally on parametric families such as the exponential family or the - power-law family, and is of interest in robust statistics ( > 1) and in constrained compression settings ( < 1). In the third part, we show an orthogonality relationship between the -power-law family and an associated linear family. As a consequence of this, the minimization of I (P; ), when the second argument comes from an -power-law family, can be shown to be equivalent to a minimization of I ( ; R), for a suitable R, where the first argument comes from a linear family. The latter turns out to be a simpler problem of minimization of a quasi convex objective function subject to linear constraints. Standard techniques are available to solve such problems, for example, via a sequence of convex feasibility problems, or via a sequence of such problems but on simpler single-constraint linear families.

Our vision is as yet unsurpassed by machines because of the sophisticated representations of objects in our brains. This representation is vastly different from a pixel-based representation used in machine storages. It is this sophisticated representation that enables us to perceive two faces as very different, i.e, they are far apart in the “perceptual space”, even though they are close to each other in their pixel-based representations. Neuroscientists have proposed distances between responses of neurons to the images (as measured in macaque monkeys) as a quantification of the “perceptual distance” between the images. Let us call these neuronal dissimilarity indices of perceptual distances. They have also proposed behavioural experiments to quantify these perceptual distances. Human subjects are asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball is taken as a measure of perceptual distance between the oddball and the distractor. Let us call such estimates as behavioural dissimilarity indices. In this thesis, we describe a decision-theoretic model for visual search that suggests a connection between these two notions of perceptual distances. In the first part of the thesis, we model visual search as an active sequential hypothesis testing problem. Our analysis suggests an appropriate neuronal dissimilarity index which correlates strongly with the reciprocal of search times. We also consider a number of alternative possibilities such as relative entropy (Kullback-Leibler divergence), the Chernoff entropy and the L1-distance associated with the neuronal firing rate profiles. We then come up with a means to rank the various neuronal dissimilarity indices based on how well they explain the behavioural observations. Our proposed dissimilarity index does better than the other three, followed by relative entropy, then Chernoff entropy and then L1 distance. In the second part of the thesis, we consider a scenario where the subject has to find an oddball image, but without any prior knowledge of the oddball and distractor images. Equivalently, in the neuronal space, the task for the decision maker is to find the image that elicits firing rates different from the others. Here, the decision maker has to “learn” the underlying statistics and then make a decision on the oddball. We model this scenario as one of detecting an odd Poisson point process having a rate different from the common rate of the others. The revised model suggests a new neuronal dissimilarity index. The new dissimilarity index is also strongly correlated with the behavioural data. However, the new dissimilarity index performs worse than the dissimilarity index proposed in the first part on existing behavioural data. The degradation in performance may be attributed to the experimental setup used for the current behavioural tasks, where search tasks associated with a given image pair were sequenced one after another, thereby possibly cueing the subject about the upcoming image pair, and thus violating the assumption of this part on the lack of prior knowledge of the image pairs to the decision maker. In conclusion, the thesis provides a framework for connecting the perceptual distances in the neuronal and the behavioural spaces. Our framework can possibly be used to analyze the connection between the neuronal space and the behavioural space for various other behavioural tasks.

The diploma thesis describes the bootstrap method and its applications in the estimate accuracy statement, in the confidence intervals generation and in the testing of statistical hypotheses. Further the method of the discrete probability estimation of the categorical quantity is presented, making use the gradient of the quasi-norm hereof distribution. On concrete examples the bootstrap method is applied in the confidence intervals forming of the categorical quantity probability function. The diploma thesis was supported by the project of MŠMT of the Czech Republic no. 1M06047 "Centre for Quality and Reliability of Production", by the grant of Grant Agency of the Czech Republic (Czech Science Foundation) reg. no. 103/08/1658 "Advanced optimum design of composed concrete structures" and by the research plan of MŠMT of the Czech Republic no. MSM0021630519 "Progressive reliable and durable structures".

The diploma thesis describes the bootstrap method and its applications in the confidence intervals generation, in the testing of statistical hypotheses and in the regression analysis. We present the confidence interval for individual value. Further the method of discrete probability estimation of the categorical quantity is presented, making use the gradient and the line estimate.

The master’s thesis deals with trading the currency market. The aim of thesis is the creation of an automated trading system based on technical analysis. This thesis is divided into several parts. The theoretical aspects and analysis of current situation are followed by automated trading system proposal. The system is designed on basis of technical indicators and tested on historical data and then optimized.

In this thesis we study the properties of exoplanetary atmospheres. The first part describes methods for searching exoplanets, statistics of discovered exoplanets and the sampling factors. The second part describes the properties of chosen planets and moons in the Solar system (Venus, Mars and Titan) and also possible properties of the exoplanetary atmospheres that are only briefly understood. The third part describes the atmospheric models which incorporate a full 3D model of the atmosphere, and a shallow- water model. We also show the results of exoplanetary atmospheric models published in the scientific journals. This part also describes the icosahedral geodetic grid that is advantageous for the global climatic models, and also discretisation on sphere and the application of the operators (gradient, divergence, vorticity) on geodetic grid. The last part is about creating program for global shallow water model in divergence-vorticity variables with forcing system with using icosahedral geodetic grid - we describe technical properties connected with model creating, parameters which the model uses during time integration, geographic system for results display and we show results for various kinds of extrasolar planets and planets in the Solar system. We used several numerical tests for testing model...