In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty in autoregressive moving average (ARMA) time series models and dynamic linear models (DLM). Bayesian model uncertainty is handled in a parametric fashion through the use of posterior model probabilities computed via Markov chain Monte Carlo (MCMC) simulation techniques. Attention is focused on reversible jump Markov chain Monte Carlo (RJMCMC) samplers, which can move between models of different dimensions, to address the problem of model order uncertainty and strategies for proposing efficient sampling schemes in autoregressive moving average time series models and dynamic linear models are developed. The general problem of assessing convergence of the sampler in a dimension-changing context is addressed by computing estimates of the probabilities of moving to higher and lower dimensional spaces. Graphical and numerical techniques are used to compare different updating schemes. The methodology is illustrated by applying it to both simulated and real data sets and the results for the Bayesian model selection and parameter estimation procedures are compared with the classical model selection criteria and maximum likelihood estimation.

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

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參考從2001到2005在台灣股市的股息對股票的影響的研究，在根據event-study and SAR method這兩種方式之實證研究可以得到本次的台灣股市裡面除權的效應。 / Referring to our research into the ex-dividend effects on stocks traded on TSE from 2001 to 2005, we come to the following conclusions under the event-study and SAR method. There should be negative ARs on the three days prior to the ex-date and a positive AR on the ex-dividend day, no matter in a bear or bull market. Stocks with cash-only dividends present lower ARs on the three days prior to the ex-date and the ex-dividend day while stock-only dividend ones suffer from higher ARs. The performances of stocks with balanced dividends are just in-between. The most significant ex-dividend effects turn up when it comes to stocks which go ex-dividend in season, with a positive CAR to the seventh day after the ex-date. On the other hand, the effects on the early-ex-dividend stocks exhibit insignificance generally. Later-ex-dividend stocks demonstrate the lowest fluctuation of ARs. However, the simultaneous decline of the index in the ex-dividend season is likely to result in higher-significant ARs. In terms of industry, the ex-dividend effects on electronic companies are more significant than on non-electronic companies. Given a bear market, there used to be negative CARs on electronic companies after shareholders’ meeting; on the contrary, in a bull market, there were positive CARs.

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Development of high average power lasers has historically been limited by the properties of available gain media. As a result it is either too costly or impractical to employ lasers in many applications for which they would otherwise be well suited. We have synthesized a new type of colloidal laser gain material that should possess many of the advantages of solid state media without their primary disadvantage: poor thermal performance. The colloid consisted of an emulsion of 20% Nd+3 doped phosphate glass nanoparticles suspended in nonanoic acid. The spectroscopic properties of the material were found to be consistent with those of bulk Nd+3 doped materials and suitable for laser development. / text

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The planning and specification of rail grinding activities using measured rail profiles normally involves a comparison between the existing and desired rail profiles within a rail segment. In current practice, a somewhat subjective approach is used to select a measured profile – usually located near the midpoint of the segment – that represents the profiles throughout the rail segment. An automated procedure was developed to calculate a representative average (mean) rail profile for a rail segment using industry-standard rail profile data. The procedure was verified by comparing the calculated average to an expected profile. The procedure was then validated by comparing the calculated average profiles of 42 in-service rail segments (10 tangents and 32 curved segments) to the corresponding subjectively chosen median rail profiles for each segment. Overall, the validation results indicated that the coordinates comprising the mean and median profiles differed by less than one percent on average. As expected, stronger agreement was observed for tangent rail segments compared to curved rail segments. Thus, the validation demonstrated that the procedure produces comparable results to current practice while improving the objectivity and repeatability of the decisions that support rail-grinding activities. / February 2017