Thesis (Ph.D.) in Spatial Information Science and Engineering--University of Maine, 2002. / Includes vita. Includes bibliographical references (leaves 151-161 ).

Water quality of small streams in agricultural landscape is mostly classified as polluted or very polluted. The reason of enhanced mineralization of organic mass in soil can be found in changed physical, chemical and biochemical properties of soil profile, which negatively display in mineralization of organic mass and nitrogen release to soil water. Analysis of water quality in agricultural river-basin prove that quality of surface water in areas with high nitrate loadings did not significantly improve in recent years. Nitrates in water are a distinct anthropogenic factor, which defines well disturbance in natural environment. This thesis deals with analysis of time series of nitrate concentrations in the Kopaninsky stream catchment in Bohemo-Moravian Highland. Further the progress in nitrate concentration and its seasonal component is researched.

With the influx of high-dimensional data there is an immediate need for statistical methods that are able to handle situations when the number of predictors greatly exceeds the number of samples. One such area of growth is in examining how environmental exposures to toxins impact the body long term. The cytokinesis-block micronucleus assay can measure the genotoxic effect of exposure as a count outcome. To investigate potential biomarkers, high-throughput assays that assess gene expression and methylation have been developed. It is of interest to identify biomarkers or molecular features that are associated with elevated micronuclei (MN) or nuclear bud (Nbud) frequency, measures of exposure to environmental toxins. Given our desire to model a count outcome (MN and Nbud frequency) using high-throughput genomic features as predictors, novel methods that can handle over-parameterized models need development. Overdispersion, when the variance of a count outcome is larger than its mean, is frequently observed with count response data. For situations where overdispersion is present, the negative binomial distribution is more appropriate. Furthermore, we expand the method to the longitudinal Poisson and longitudinal negative binomial settings for modeling a longitudinal or clustered outcome both when there is equidispersion and overdispersion. The method we have chosen to expand is the Generalized Monotone Incremental Forward Stagewise (GMIFS) method. We extend the GMIFS to the negative binomial distribution so it may be used to analyze a count outcome when both a high-dimensional predictor space and overdispersion are present. Our methods were compared to glmpath. We also extend the GMIFS to the longitudinal Poisson and longitudinal negative binomial distribution for analyzing a longitudinal outcome. Our methods were compared to glmmLasso and GLMMLasso. The developed methods were used to analyze two datasets, one from the Norwegian Mother and Child Cohort study and one from the breast cancer epigenomic study conducted by researchers at Virginia Commonwealth University. In both studies a count outcome measured exposure to potential genotoxins and either gene expression or high-throughput methylation data formed a high dimensional predictor space. Further, the breast cancer study was longitudinal such that outcomes and high-dimensional genomic features were collected at multiple time points during the study for each patient. Our goal is to identify biomarkers that are associated with elevated MN or NBud frequency. From the development of these methods, we hope to make available more comprehensive statistical models for analyzing count outcomes with high dimensional predictor spaces and either cross-sectional or longitudinal study designs.

The Czech Republic's economic performance is measurable by some macroeconomic indicators which have made variable progress in recent years. Based on general economic conditions, economic development has impacted upon economic results of companies. Over time their progress is recorded by economic time series, which describe it. Through the agency of economic time series, economic development and mutual dependences among indicators can be researched. This problem can be solved by applying the methodology which helps describe and quantify relations among quantities. For the purpose of expression of a single time series, stochastic linear modelling is used, and for quantifying the strength of relation among time series, regression analyses and Granger causality testing are used.

The diploma thesis deals with the analysis of economic indicators of Technos a.s., using statistical methods and the evaluation of the current financial situation. The work contains a theoretical and practical part. The theoretical part describes selected economic indicators, regression analysis, time series and correlation analysis. In the practical part, the analysis of selected economic indicators will be carried out and then statistical methods will be used to determine the prediction for the next year and to reveal the dependence between the individual indicators. The last part of the thesis deals with proposals that will improve the financial situation of the company.

The diploma thesis focuses on the design of an application that will be used for the analysis of financial indicators. The application allows automatic calculation of financial indicators and regression analysis. It also allows a detailed analysis of calculated financial indicators using time series.

No description available.

<p>In this thesis, we apply the Ordinary Least Squares (OLS) and the Generalized Least Squares (GLS) methods for the estimation of Autoregressive Conditional Duration (ACD) models, as opposed to the typical approach of using the Quasi Maximum Likelihood Estimation (QMLE).</p> <p>The advantages of OLS and GLS as the underlying methods of estimation lie in their theoretical ease and computational convenience. The latter property is crucial for high frequency trading, where a transaction decision needs to be made within a minute. We show that both OLS and GLS estimates are asymptotically consistent and normally distributed. The normal approximation does not seem to be satisfactory in small samples. We also apply Residual Bootstrap to construct the confidence intervals based on the OLS and GLS estimates. The properties of the proposed methods are illustrated with intensive numerical simulations as well as by a case study on the IBM transaction data.</p> / Master of Science (MSc)

Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time.

Pension Insurance is one of the main pillars of the Czech social security system. A significant part of this system are also invalidity pension and survivor pension, which serve as financial compensation in case of sudden individual's work ability decrease (invalidity pension), or sudden death (survivors pension). This paper deals with legislation concerning invalidity and survivors pensions and the procedure of calculating those benefits in the Czech Republic. The main analytical part introduces the reader to the amount of expenditures of analyzed pensions, their development in the past and expected future development. This work also introduces the reader to the factors that influence the number of pensions. Attached is the invalidity and survivors pension calculator in MS Excel 2007.