James Keenan served as United States consul to Hong Kong for eight years beginning in 1853. Keenan's career demonstrated the difficulties faced by United States consuls in the Far East. Many of the problems Keenan faced during his career resulted from the juxtaposition of a man predisposed to controversy with one of the most ambiguous posts in United States consular service. Keenan's career involved him in difficulties with a United States naval commander, British authorities in Hong Kong, a United States commissioner to China, his temporary successor in Hong Kong, and even the State Department. During his career, Keenan anticipated legislative changes regarding United States consuls. Nevertheless Keenan's colorful career won him many British and American friends. However, his predeliction for controversy damaged his effectiveness as United States consul.

In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process.  In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then study both in-sample and out-of-sample forecast performance of this model by comparing it with the linear SARIMA model. Based on the in-sample forecast assessment from the linear SARIMA and the nonlinear SETAR models, the forecast measure MAE and RMSE suggest that the nonlinear SETAR model outperform the linear SARIMA model. Also using multi-step-ahead forecast method we predicted and compared the out-of-sample forecast of the linear SARIMA and the nonlinear SETAR models over the forecast horizon of 12 months during the period of 2010:1 to 2010:12. From the results as suggested by MAE and RMSE, the forecast performance of the nonlinear SETAR models is superior to that of the linear SARIMA model in forecasting Ghana inflation rates. Thought the nonlinear SETAR model is superior to the SARIMA model according to MAE and RMSE measure but using Diebold-Mariano test, we found no significant difference in their forecast accuracy for both in-sample and out-of-sample.

In this thesis electromagnetic wave propagation is investigated in an indoor environment. The indoor environment is a furnished office building with corridors, corners and rooms. Particularly, there is an atrium through the building in the center. For the study there were measurements available from real building in the 2.1 GHz frequency band. One objective is to design a propagation model that should be simple but reflect the trend of the propagation measurements. Furthermore, a system performance evaluation is carried out based on the implemented model. The proposed 3D model is a combination of the Free Space Path Loss model, the Keenan-Motley model and the recursive diffraction model. The channel predictions from the 2D Keenan-Motley algorithm are quite different from the measurements. Therefore, the 3D Keenan-Motley algorithm is designed to depict the atrium effect and speed up the simulation at the same time. Besides a buttery radiation diagram is created to mimic Kathrein 80010709 antenna installed in the building. Finally, a diffracted path is added to improve the received signal strength for the users around the atrium areas. With all the above procedures, the final results from the model are in good quantitative agreement with the measurement data. With the implemented propagation model, a further analysis of the system performance on the Distributed Antenna System (DAS) is performed. A comparison for the system capacity between the closed building and the atrium building is conducted, showing that the former one benefits more when the number of the cells increases. The reason is the atrium cells suffer severe interference from neighbor cells during high traffic demand scenarios. Then some further cell configurations show that the number of the cells, the geometry performance and the balance of the user fraction should be considered to improve the system capacity.

In this study, we present two new frequency domain tests for testing the Gaussianity and linearity of a sixth-order stationary univariate time series. Both are two-stage tests. The first stage is a test for the Gaussianity of the series. Under Gaussianity, the estimated normalized bispectrum has an asymptotic chi-square distribution with two degrees of freedom. If Gaussianity is rejected, the test proceeds to the second stage, which tests for linearity. Under linearity, with non-Gaussian errors, the estimated normalized bispectrum has an asymptotic non-central chi-square distribution with two degrees of freedom and constant noncentrality parameter. If the process is nonlinear, the noncentrality parameter is nonconstant. At each stage, empirical distribution function (EDF) goodness-ofit (GOF) techniques are applied to the estimated normalized bispectrum by comparing the empirical CDF with the appropriate null asymptotic distribution. The two specific methods investigated are the Anderson-Darling and Cramer-von Mises tests. Under Gaussianity, the distribution is completely specified, and application is straight forward. However, if Gaussianity is rejected, the proposed application of the EDF tests involves a transformation to normality. The performance of the tests and a comparison of the EDF tests to existing time and frequency domain tests are investigated under a variety of circumstances through simulation. For illustration, the tests are applied to a number of data sets popular in the time series literature.