PM10 and ozone are the dominant air pollutants in the Urban Kaohsiung. Ozone is a secondary pollutant generated in the troposphere from the precursors nitrogen dioxide and non-methane hydrocarbons. The trends of ozone concentrations first statistically are summarized utilizing the monitoring data during the period 1998¡Ð2007. All data are collected from four fixed-site air quality monitoring stations in Kaohsiung City. The results show that ozone concentration in Kaohsiung has one perennial peak concentration, occurring in October and March. The highest values occur in October and the secondary high value in March. The lowest values occur in the summer. The monitor data possess timeliness of data and the non-linear dynamic tendency. Artificial Neural Network ¡]ANN¡^, a system recognition, self-study function and ability of the solution to non-linearity dynamic system problem, was used as a tool to analyze these monitor data. This work utilizing neural networks develops a model to predict the trend of ozone situations in the Urban Kaohsiung. The network was trained using meteorological factor and air quality data when the ozone concentrations are the highest. The optimum set value of five parameters including date partition, hidden layer neurons, training function, leraning rate , and momentum coefficient were obtained based on trial and error methods. The simulated results of ozone concentration have a correlation coefficient within the range 0.865¡Ð0.899 and IOA within the range 0.927¡Ð0.934. The trend results of ozone concentration reflect strong relationships in all stations. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modeling.

Τα μοντέλα παλινδρόμησης χρησιμοποιούνται ευρέως σήμερα στη διοίκηση των επιχειρήσεων, στην οικονομία, στη μηχανική, στην υγεία, τη βιολογία και τις κοινωνικές επιστήμες. Στη στατιστική, η ανάλυση παλινδρόμησης είναι μία στατιστική διαδικασία για την εκτίμηση των σχέσεων μεταξύ διαφόρων μεταβλητών. Περιέχει πολλές τεχνικές για τη μοντελοποίηση και την ανάλυση των μεταβλητών αυτών, ενώ επικεντρώνεται συνήθως στη σχέση μεταξύ μιας εξαρτημένης και μιας ή περισσοτέρων ανεξαρτήτων μεταβλητών. Η παρούσα εργασία επιδιώκει να παρουσιάσει το θεωρητικό πλαίσιο της ανάλυσης παλινδρόμησης, ξεκινώντας από το απλό μοντέλο και επεκτείνοντας την ανάλυση στο πολλαπλό, για να καταλήξει και να επικεντρωθεί στα μοντέλα συσχέτισης και συγκεκριμένα στους συντελεστές συσχέτισης και στους ελέγχους υποθέσεων αυτών. / Correlation models are widely used in social sciences biology and engineering. In this dissertation we present the theoretical framework of regression analysis and correlation models and finally we present results in real problems and applications.

This paper uses Maximum likelihood estimation method to estimate the common correlation coefficients for multivariate datasets. We discuss a graphical tool, Q-Q plot, to test equality of the common intraclass correlation coefficients. Kolmogorov-Smirnov test and Cramér-von Mises test are used to check if the intraclass correlation coefficients are the same among populations. Bootstrap and empirical likelihood methods are applied to construct the confidence interval of the common intraclass correlation coefficients.

High linoleic acid soybeans facilitate maximum production of soy-based polyurethane. The objectives of this study were to: 1) Evaluate environmental influence on yield and seed composition traits; 2) Estimate correlation coefficients between linoleic acid with agronomic traits; 3) Validate SSR markers associated with fatty acid QTL in multiple environments and across diverse genotypes; and 4) Evaluate the influence of fertilizers differing in P and K concentrations on seed fatty acids. RG25 was identified as the best genotype to be commercialized for polyurethane production. Strong marker-trait associations across environments included Satt_335, Satt389, Satt556 associated with palmitic and stearic, Satt389 with oleic, Satt389 and Satt537 with linoleic acid. A significant increase in linoleic acid content was observed when plants received modified Hoagland’s solution with 2×K compared to without K. Development of a high linoleic acid soybean line for polyurethane production is feasible using validated SSR markers and high K fertility. / Ontario Ministry of Agriculture, Food and Rural Affairs

Multivariate generalized linear mixed models (MGLMM) are used for jointly modeling the clustered mixed outcomes obtained when there are two or more responses repeatedly measured on each individual in scientific studies. The relationship among these responses is often of interest. In the clustered mixed data, the correlation could be present between repeated measurements either within the same observer or between different observers on the same subjects. This study proposes a series of in- dices, namely, intra, inter and total correlation coefficients, to measure the correlation under various circumstances of observations from a multivariate generalized linear model, especially for joint modeling of clustered count and continuous outcomes. Bayesian methods are widely used techniques for analyzing MGLMM. The need for noninformative priors arises when there is insufficient prior information on the model parameters. Another aim of this study is to propose an approximate uniform shrinkage prior for the random effect variance components in the Bayesian analysis for the MGLMM. This prior is an extension of the approximate uniform shrinkage prior. This prior is easy to apply and is shown to possess several nice properties. The methods are illustrated in terms of both a simulation study and a case example.

Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges. Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and projected potential future PV system feasibility. Artificial neural networks (ANN) have been used successfully for solar energy forecasting. In this work, several meteorological variables from Saudi Arabia as a case study will be used to determine the most effective variables on Global Solar Radiation (GSR) prediction. Those variables will be used as inputs for a proposed GSR prediction model. This model will be applicable in different locations and conditions. This model has a simple structure and offers better results in terms of error between actual and predicted solar radiation values.

The subject of this work is approaching and explanation of the methods of the intercompany comparison, further the implementation of the short financial analysis of the selected company, the comparison with other firms using the methods of the intercompany comparison in the selected branches. It explains basic terms of the financial analysis, methods of financial analysis and methods of the intercompany comparison. The practical part of the thesis showes the analysis of the financial situation of the selected company and the conducted intercompany comparison by the methods of order, rating, scoring, simplified scoring, standardized variables and distance from the fictional firm using a variable number of parameters. The resulting order of companies is evaluated by Spearman's correlation coefficient. In the end of the work there is the overall assessment of the results of the intercompany comparison and description of some problems that can occur during the calculation and implementation of the intercompany comparison.

DeMiguel, Garlappi e Uppal (2009) fizeram a comparação da regra 1/N ou de Talmud com 14 modelos de otimização que vieram depois do trabalho de Markowitz (1952). As conclusões mostraram que todos os modelos de alocação ótima analisados tiveram um desempenho inferior ao da regra de Talmud. Tu e Zhou (2011) propuseram uma combinação entre Markowitz e Talmud para que tal modelo superasse Talmud. Os resultados obtidos foram satisfatórios. A desconsideração dos eventos extremos (dependência assimétrica ou caudal) durante o processo de construção de carteiras poderá diminuir as habilidades dos gestores de ativos em reduzir o risco através da diversificação. A modelagem de cópulas sobre os retornos dos ativos nos permite calcular uma alternativa para medir a dependência dos ativos em eventos extremos através do índice de dependência caudal inferior. Hatherley e Alcock (2007) relataram que o modelo de Markowitz tende a subestimar as perdas potenciais que venham a ocorrer na presença de eventos extremos de mercado (crashes) para um determinado nível de retorno esperado. Verificamos se as estratégias com dependência caudal superaram Talmud, o modelo de Markowitz e o modelo de Tu e Zhou (2011) através da simulação de 1.000 carteiras com 3, 5, 10 e 20 ativos escolhidos ao acaso do índice DJIA no período de 03/1990 até 12/2012. Concluímos que os modelos de dependência caudal e o de Markowitz tiveram uma desempenho fora da amostra superior ao Talmud e ao modelo de Tu e Zhou (2011) para as carteiras com 3, 5, 10 e 20 ativos. A estratégia com dependência caudal superou Markowitz, em termos de retorno acumulado, em mais de 60% dos meses considerados em todas as análises. Os resultados apontam que a regra de Talmud deve ser descartada num contexto de construção de carteiras com ações frente à estratégia com dependência caudal. / DeMiguel, Garlappi and Uppal (2009) made a comparison of rule 1 / N or Talmud with most optimization techniques that followed the work of Markowitz (1952). The conclusions were devastating for all asset allocation models in the context of portfolios combined with other portfolios. Tu and Zhou (2011) proposed a combination between Markowitz and Talmud to overcome such a rule Talmud. The results were satisfactory. In the presence of extreme events, the Pearson correlation coefficient tends to increase in magnitude, making spurious results diversification based solely on this factor. The elimination of extreme events (asymmetric or tail dependence) during the portfolio construction process can reduce the skills of asset managers to reduce risk through diversification. The copula theory allows us to calculate an alternative to measure the dependence of extreme events in assets through the index lower tail dependence. Hatherley and Alcock (2007) reported that the Markowitz model tends to underestimate the potential losses that may occur in the presence of extreme market events (crashes) for a given level of expected return. We check that the strategies with tail dependence overcame Talmud rule, the Markowitz model and the model of Tu and Zhou (2011) by simulating 1,000 portfolios with 3, 5, 10 and 20 randomly selected assets from DJIA for the period 03/1990 until 12/2012. We conclude that models of tail dependence and Markowitz had more performance ex-ante than Talmud and the Tu and Zhou (2011) model for portfolios with 3, 5, 10 and 20 assets. Tail dependence models overcome Markowitz, in terms of cumulative return, in over 60% of months considered in the analysis. The results indicate that the Talmud rule should be discarded in a context of constructing portfolios with individual stocks ahead strategies with tail dependence.

This dissertation: (a) investigated the degree to which the squared canonical correlation coefficient is biased in multivariate nonnormal distributions and (b) identified formulae that adjust the squared canonical correlation coefficient (Rc2) such that it most closely approximates the true population effect under normal and nonnormal data conditions. Five conditions were manipulated in a fully-crossed design to determine the degree of bias associated with Rc2: distribution shape, variable sets, sample size to variable ratios, and within- and between-set correlations. Very few of the condition combinations produced acceptable amounts of bias in Rc2, but those that did were all found with first function results. The sample size to variable ratio (n:v)was determined to have the greatest impact on the bias associated with the Rc2 for the first, second, and third functions. The variable set condition also affected the accuracy of Rc2, but for the second and third functions only. The kurtosis levels of the marginal distributions (b2), and the between- and within-set correlations demonstrated little or no impact on the bias associated with Rc2. Therefore, it is recommended that researchers use n:v ratios of at least 10:1 in canonical analyses, although greater n:v ratios have the potential to produce even less bias. Furthermore,because it was determined that b2 did not impact the accuracy of Rc2, one can be somewhat confident that, with marginal distributions possessing homogenous kurtosis levels ranging anywhere from -1 to 8, Rc2 will likely be as accurate as that resulting from a normal distribution. Because the majority of Rc2 estimates were extremely biased, it is recommended that all Rc2 effects, regardless of which function from which they result, be adjusted using an appropriate adjustment formula. If no rationale exists for the use of another formula, the Rozeboom-2 would likely be a safe choice given that it produced the greatest number of unbiased Rc2 estimates for the greatest number of condition combinations in this study.

Suppose two or more variables are jointly normally distributed. If there is a common relationship between these variables it would be very important to quantify this relationship by a parameter called the correlation coefficient which measures its strength, and the use of it can develop an equation for predicting, and ultimately draw testable conclusion about the parent population. This research focused on the correlation coefficient ρ for the bivariate and trivariate normal distribution when equal variances and equal covariances are considered. Particularly, we derived the maximum Likelihood Estimators (MLE) of the distribution parameters assuming all of them are unknown, and we studied the properties and asymptotic distribution of . Showing this asymptotic normality, we were able to construct confidence intervals of the correlation coefficient ρ and test hypothesis about ρ. With a series of simulations, the performance of our new estimators were studied and were compared with those estimators that already exist in the literature. The results indicated that the MLE has a better or similar performance than the others.