The aim of this thesis is to incorporate the effects of the indirect trade into the gravity model for Czech Republic. Using data from the recently released OECD-WTO TiVA database, a panel of 56 countries in 5 years between 1995 and 2009 is constructed. The traditional approach of estimating the log- linear form of the equation is questioned and in line with current academic research, the Poisson Pseudo Maximum Likelihood method is applied. The empirical analysis does not reveal any unambiguous effect of adjusting the gross exports for their foreign content; it rather confirms that Czech ex- ports are significantly driven by the demand for German exports and finds that they are the higher the greater is the share of services value added. Furthermore, it is found that the destination of Czech exports is not signif- icantly determined by target country's participation in global value chains. JEL Classification C13, C23, C67, F14, F60 Keywords gravity model, indirect trade, trade in value added, Czech Republic, Poisson regression, panel data Author's e-mail marek.wlazel@gmail.com Supervisor's e-mail vsemerak@yahoo.com

Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results show that the proposed estimation methods work well and improve the estimating process.

Este trabalho estuda o impacto de choques emocionais induzidos por resultados de jogos de futebol entre 2006 e 2016 no Brasil no comportamento violento dos indivíduos. Assume-se que o risco de violência pode ser modelado como função dos resultados das partidas. A hipótese chave é de que condicionando pelas probabilidades de vitória atribuídas aos times antes dos jogos, dadas pelos mercados de apostas, os resultados das partidas podem ser interpretados como aleatórios. Utiliza-se o fato de que torcidas de times de futebol do Brasil estão bem espalhadas ao longo dos municípios do país (e não apenas restritas ao estado de origem do time) para captar a proporção de pessoas atingidas por choques emocionais causados por partidas de futebol em determinado dia. O choque emocional é construído pela diferença entre a proporção de pessoas no município atingidas por choques positivos e a proporção atingida por choques negativos. Considera-se um choque o resultado da partida que foi muito diferente do previsto pelo mercado de apostas. O resultado encontrado indica que cada ponto percentual a mais no número de pessoas recebendo um choque negativo em determinado município eleva o número esperado de mortes por agressão em 0,08%. A análise extensiva do modelo indica que o efeito é mais intenso para óbitos masculinos, principalmente àqueles ocorridos na rua. O número de mortes femininas ocorridas em casa aumenta de forma significante diante de choques negativos. / This project studies the impact of emotional shocks induced by the results of brazilian soccer games in the period from 2006 from 2016 in the violent behavior of individuals. I assume the risk of violence being a function of the soccer games results. The key hypotesis is that conditional on the implied probabilities given by betting markets before the game, the actual results can be seen as random. I use the fact of soccer clubs distribution along the country being very spread, not only concentrated in the local state of the club (as happens in the NFL distribution of supporters) so I can mesure the proportion of people being affected by emotional shocks induced by soccer games in a given day. The emotional shock is given by the difference between the amount of individuals affected by positive shocks and the amount affected by negative shocks. I consider a schock when the result of the match differs from the predicted by the beting markets. The baseline result indicates that each 1 p.p. in the number of individuals receiving negative shocks in a given municipallity elevates the expected number of deaths caused by agressions in 0.08%. The model extension analysis indicates that the effect is more intensive for men death, mainly the ones occuring in the streets. The number of women deaths occuring at home elevates significantly when a negative shock occurs.

There has been an increasing interest in uncovering smuggled nuclear materials associated with the War on Terror. Detection of special nuclear materials hidden in cargo containers is a major challenge in national and international security. We propose a new physics-based method to determine the presence of the spectral signature of one or more nuclides from a poorly resolved spectra with weak signatures. The method is different from traditional methods that rely primarily on peak finding algorithms. The new approach considers each of the signatures in the library to be a linear combination of subspectra. These subspectra are obtained by assuming a signature consisting of just one of the unique gamma rays emitted by the nuclei. We propose a Poisson regression model for deducing which nuclei are present in the observed spectrum. In recognition that a radiation source generally comprises few nuclear materials, the underlying Poisson model is sparse, i.e. most of the regression coefficients are zero (positive coefficients correspond to the presence of nuclear materials). We develop an iterative algorithm for a penalized likelihood estimation that prompts sparsity. We illustrate the efficacy of the proposed method by simulations using a variety of poorly resolved, low signal-to-noise ratio (SNR) situations, which show that the proposed approach enjoys excellent empirical performance even with SNR as low as to -15db. The proposed method is shown to be variable-selection consistent, in the framework of increasing detection time and under mild regularity conditions. We study the problem of testing for shielding, i.e. the presence of intervening materials that attenuate the gamma ray signal. We show that, as detection time increases to infinity, the Lagrange multiplier test, the likelihood ratio test and Wald test are asymptotically equivalent, under the null hypothesis, and their asymptotic null distribution is Chi-square. We also derived the local power of these tests. We also develop a nonparametric approach for detecting spectra indicative of the presence of SNM. This approach characterizes the shape change in a spectrum from background radiation. We do this by proposing a dissimilarity function that characterizes the complete shape change of a spectrum from the background, over all energy channels. We derive the null asymptotic test distributions in terms of functionals of the Brownian bridge. Simulation results show that the proposed approach is very powerful and promising for detecting weak signals. It is able to accurately detect weak signals with SNR as low as -37db.

This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below. 1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in scholarly publications. Less frequently used are joint grant proposals and patents. Many scholars believe that co-authorship as the sole measure of research collaboration is insufficient because collaboration between researchers might not result in co-authorship. Collaborations involve informal communication (i.e., conversational exchange) between researchers. Using self-reports from 100 tenured/tenure-track faculty in the College of Engineering at the University of South Florida, researchers' networks are constructed from their communication relations and collaborations in three areas: joint publications, joint grant proposals, and joint patents. The data collection: 1) provides a rich data set of both researchers' in-progress and completed collaborative outputs, 2) yields a rating from the researchers on the importance of a tie to them 3) obtains multiple types of ties between researchers allowing for the comparison of their multiple networks. Exponential Random Graph Model (ERGM) results show that the more communication researchers have the more likely they produce collaborative outputs. Furthermore, the impact of four demographic attributes: gender, race, department affiliation, and spatial proximity on collaborative output relations is tested. The results indicate that grant proposals are submitted with mixed gender teams in the college of engineering. Besides, the same race researchers are more likely to publish together. The demographics do not have an additional leverage on joint patents. 2. Study II: Previous research shows that researchers' social network metrics obtained from a collaborative output network (e.g., joint publications or co-authorship network) impact their performance determined by g-index. This study uses a richer dataset to show that a scholar's performance should be considered with respect to position in multiple networks. Previous research using only the network of researchers' joint publications shows that a researcher's distinct connections to other researchers (i.e., degree centrality), a researcher's number of repeated collaborative outputs (i.e., average tie strength), and a researchers' redundant connections to a group of researchers who are themselves well-connected (i.e., efficiency coefficient) has a positive impact on the researchers' performance, while a researcher's tendency to connect with other researchers who are themselves well-connected (i.e., eigenvector centrality) had a negative impact on the researchers' performance. The findings of this study are similar except that eigenvector centrality has a positive impact on the performance of scholars. Moreover, the results demonstrate that a researcher's tendency towards dense local neighborhoods (as measured by the local clustering coefficient) and the researchers' demographic attributes such as gender should also be considered when investigating the impact of the social network metrics on the performance of researchers. 3. Study III: This study investigates to what extent researchers' interactions in the early stage of their collaborative network activities impact the number of collaborative outputs produced (e.g., joint publications, joint grant proposals, and joint patents). Path models using the Partial Least Squares (PLS) method are run to test the extent to which researchers' individual innovativeness, as determined by the specific indicators obtained from their interactions in the early stage of their collaborative network activities, impacts the number of collaborative outputs they produced taking into account the tie strength of a researcher to other conversational partners (TS). Within a college of engineering, it is found that researchers' individual innovativeness positively impacts the volume of their collaborative outputs. It is observed that TS positively impacts researchers' individual innovativeness, whereas TS negatively impacts researchers' volume of collaborative outputs. Furthermore, TS negatively impacts the relationship between researchers' individual innovativeness and the volume of their collaborative outputs, which is consistent with `Strength of Weak Ties' Theory. The results of this study contribute to the literature regarding the transformation of tacit knowledge into explicit knowledge in a university context.

<p>This Dissertation includes a statistical analysis of traffic accidents followed by a test to know the effect of new pavement on traffic safety. The accident data is considered for the roads those are in Region South-East Sweden that got new pavement during the year 2001. In Sweden, this is the fourth study concerning the before and after effect of the new pavement. Johansson (1997) studied the change in the number of accidents between the before-years and after-years. Tholén (1999) and Velin et al (2002) have additionally compared the change with the change in the number of accidents in a reference road network (also called control sites) consisting of all public roads in Region West Sweden which were not resurfaced during the study period.</p>

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This Dissertation includes a statistical analysis of traffic accidents followed by a test to know the effect of new pavement on traffic safety. The accident data is considered for the roads those are in Region South-East Sweden that got new pavement during the year 2001. In Sweden, this is the fourth study concerning the before and after effect of the new pavement. Johansson (1997) studied the change in the number of accidents between the before-years and after-years. Tholén (1999) and Velin et al (2002) have additionally compared the change with the change in the number of accidents in a reference road network (also called control sites) consisting of all public roads in Region West Sweden which were not resurfaced during the study period.

This project examined the extent to which Nova Scotian adolescents’ counselling needs are being met with respect to physical, sexual, substance use, and psychosocial health by their family physicians. This was accomplished by assessing how well Nova Scotian physicians provide preventative advice consistent with the Guidelines for Adolescent Preventative Services (GAPS). Analyses were performed using pooled data from surveys carried out in 2003 and 2006. Descriptive analyses, Poisson and logistic regression were used to examine associations of sociodemographic characteristics, need, and the presence of school based health centres (SBHCs) with the provision of advice. Advice was not well provided and appeared to be need-driven. Females were significantly more likely to be provided advice and respondent access to a SBHC increased the likelihood of advice being provided. These results have implications for policy and practice, specifically, ways to refine preventative healthcare services for the province’s adolescents to ensure optimal care.

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.