Thesis (M.SC.) - Carleton University, 2007. / Includes bibliographical references (p. 51-52). Also available in electronic format on the Internet.

Thesis (M.A.) in Mathematics and Statistics--University of Maine, 2002. / Includes vita. Includes bibliographical references (leaf 45).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 152-153).

Poorly integrated crime analysis may be a detriment to crime reduction efforts and financial resources. The purpose of this research is to identify deficiencies and successes in crime analysis integration and to understand which agency factors are related. Using the Stratified Model of Problem Solving, Analysis, and Accountability and data from a national PERF survey of police agencies, this study quantifies the levels of production and consumption-based integration disconnect as well as other important agency factors. To determine which agency factors contribute most to integration disconnect, bivariate correlation and multiple regression analyses are used to examine the relationships, while controlling for agency type, centralization, officers per analyst, crimes per officer, and agency size. Findings indicate that production- and consumption-based disconnect are positively related to one another and that passive patrol-analyst interactions, an agency’s analysis integration disconnect. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Saudi Arabia is the largest petroleum producer and exporter in the world. Saudi Arabian economy hugely depends on production and export of oil. This motivates us to do research on oil production of Saudi Arabia. In our research the prime objective is to find the most appropriate models for analyzing Saudi Arabia oil production data. Initially we think of considering integrated autoregressive moving average (ARIMA) models to fit the data. But most of the variables under study show some kind of volatility and for this reason we finally decide to consider autoregressive conditional heteroscedastic (ARCH) models for them. If there is no ARCH effect, it will automatically become an ARIMA model. But the existence of missing values for almost each of the variable makes the analysis part complicated since the estimation of parameters in an ARCH model does not converge when observations are missing. As a remedy to this problem we estimate missing observations first. We employ the expectation maximization (EM) algorithm for estimating the missing values. But since our data are time series data, any simple EM algorithm is not appropriate for them. There is also evidence of the presence of outliers in the data. Therefore we finally employ robust regression least trimmed squares (LTS) based EM algorithm to estimate the missing values. After the estimation of missing values we employ the White test to select the most appropriate ARCH models for all sixteen variables under study. Normality test on resulting residuals is performed for each of the variable to check the validity of the fitted model. / ARCH/GARCH models, outliers and robustness : tests for normality and estimation of missing values in time series -- Outlier analysis and estimation of missing values by robust EM algorithm for Saudi Arabia oil production data -- Selection of ARCH models for Saudi Arabia oil production data. / Department of Mathematical Sciences

For the last two decades, demand for poultry meat has been soared, since it is healthier and less costly than its substitutes. In order to meet this increasing demand, integrated poultry organizations have been established all over the world. Usually, an integrated poultry organization has the divisions of breeder coops, incubation house, broiler coops, feed mill, slaughterhouse and marketing. This complex structure makes production planning activities more difficult for integrated poultry organizations. The aim of this study is to propose a production and financial planning model for &Ouml / nder Integrated Poultry Incorporation using mathematical modelling techniques and statistical methods.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 174-178). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 174-178). Also available on the Internet.

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 207-211). Also available in electronic format on the Internet.

The statistical assessment of harvested fish populations, such as the Atlantic-Iberian sardine (AIS) stock, needs to deal with uncertainties inherent in fisheries systems. Uncertainties arising from sampling errors and stochasticity in stock dynamics must be incorporated in stock assessment models so that management decisions are based on realistic evaluation of the uncertainty about the status of the stock. The main goal of this study is to develop a stock assessment framework that accounts for some of the uncertainties associated with the AIS stock that are currently not integrated into stock assessment models. In particular, it focuses on accounting for the uncertainty arising from the catch data sampling process. The central innovation the thesis is the development of a Bayesian integrated stock assessment (ISA) model, in which an observation model explicitly links stock dynamics parameters with statistical models for the various types of data observed from catches of the AIS stock. This allows for systematic and statistically consistent propagation of the uncertainty inherent in the catch sampling process across the whole stock assessment model, through to estimates of biomass and stock parameters. The method is tested by simulations and found to provide reliable and accurate estimates of stock parameters and associated uncertainty, while also outperforming existing designed-based and model-based estimation approaches. The method is computationally very demanding and this is an obstacle to its adoption by fisheries bodies. Once this obstacle is overcame, the ISA modelling framework developed and presented in this thesis could provide an important contribution to the improvement in the evaluation of uncertainty in fisheries stock assessments, not only of the AIS stock, but of any other fish stock with similar data and dynamics structure. Furthermore, the models developed in this study establish a solid conceptual platform to allow future development of more complex models of fish population dynamics.