National economic development relies on long-term stable supply of electricity, and the stability of electricity supply absolutely relates to the quality of maintenance of power generation units. In the past, the electricity supply of Taiwan was exclusively dominated by the state-run Taiwan Power Company (Taipower). Following government de-regulation policy, the private IPP (Independent Power Plants) also have joined the grid of Taiwan's electricity supply that diversified electricity power supply. This study collects power generation equipment maintenance cost information bases on practical detail cost information and labor cost paid by employer that listed in law or regulation. The information of estimating contrast cost which cannot be easily quantified is collected by questionnaire of power plant and contractor personnel who response for estimating cost of power generation equipment maintenance in order to gather practical cost estimating rules. After summarizing and analyzing collected maintenance cost data, easily calculating cost estimating model for power generation equipment had been proposed. The model can be set as criteria for cost estimating and examine the maintenance contract price of contractor in view of contractor cost. Power generation industries had faced severe cost pressures due to the high rising fuel costs, but not readily reflect the cost of power generation to the electricity charges. In the condition of high price of fuel, the only way to save operational cost was to reduce expense of equipment maintenance and other operation costs. So, the power plants aim at lowing equipment maintenance costs to meet limited budget. Most power plants plan their equipment maintenance budget according to quotation of original equipment manufacturer or professional contractor. There are lacks of related cost information to examine whether the quotation is reasonable or not. The study showed that the scale of contractor companies, indirect cost apportionment can affect indirect costs of contract cost, thereby affecting the contract price. The estimated profit will vary with many factors case by case such as market competition level, technical difficulty, working season, and duration etc.

published_or_final_version / Mathematics / Master / Master of Philosophy

In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed. Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model. Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

This research describes an investigation into developing closer working relationships through project partnering in order to reduce the occurrence of adversarial practices commonly found between main contractors and subcontractors. A literature review was undertaken that identified two main types of partnering: project partnering; and strategic partnering. One of the main differences between the two types of partnering were identified as being their durations: short term (project partnering); and long term (strategic partnering). It was concluded that the published literature had the following limitations: primarily applicable to the Australian and US construction industries; models and processes identified did not have an adequate practical content; and limited application to the main contractor - SC relationship. After completion of the literature review a research methodology was developed, allowing an alternative approach to project partnering to be developed. The research methodology enabled information to be obtained from both the collaborating contractors personnel and SC personnel. The approach to project partnering was named 'semi project partnering' as it contained an element of SC competition. The approach was implemented on a live commercial project during the preconstruction tendering and estimating stages. The semi project partnering approach utilised both the knowledge gained from the literature review and the conclusions from the empirical research. The approach was validated by the collaborating company's personnel. Further validation of the achievements of the approach took place by interviewing personnel from both the collaborating company and subcontractors. The main outcomes from the research are: identification of what a main contractor's employees want from their dealings with subcontractors; recognition of what subcontractors want from their dealings with main contractors; a comparison of the collaborating companies performance compared to that of its competitors; and a semi project partnering approach that provides: a reduction in the occurrence of adversarial practices used between main contractors and subcontractors; and earlier involvement of subcontractors within the building process promoting mutual understanding.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 64-66). Also available on the World Wide Web.

Thesis (M.U.A Pl.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 66-68). Also available via the Internet.

Thesis (Ph.D.)--Hong Kong Polytechnic University, 2007. / Adviser: Wilson Tong. Includes bibliographical references.

Thesis (Ph. D. in Leadership and Policy Studies)--Vanderbilt University, Aug. 2006. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--University of Nevada, Reno, 2006. / "December, 2006." Includes bibliographical references (leaves 38-40). Online version available on the World Wide Web.

This work was undertaken with a view to construct a macroeconometric model for the Indian economy for purposes of forecasting and policy simulation. As a prelude to this exercise, we have surveyed available forecasting techniques, techniques of evaluation of forecasts and forecasting models and the issues concerning the use of macroeconometric models in the context of policy analysis. We have also considered specific issues and considerations relevant in the context of developing countries. As a second step towards providing a proper perspective to our model, and to derive useful guidelines, we have surveyed and reviewed existing macro-econometric models of the Indian economy. This survey concerns the models built by Narasimham, Choudhry, Krishnamurty, Krishnamurty-Choudhry, Marwah, Mammen, Agarwala, Pandit, Gupta, Bhattacharya and UNCTAD. We notice that an interesting variety of sectoral emphasis is offered in these models although in general they are all based on the IS-LM framework. As a part of the review of the existing models, we have re-estimated three models, viz., models by Choudhry, Marwah and Bhattacharya under common sample conditions and estimation techniques and have compared their forecasting performance against alternative autoregressive benchmark models. In general, the benchmark models do better but the performance of Bhattacharya and Marwah models, in their adapted versions, seems to be satisfactory. A common shortcoming of all these models is an underexploration of the fiscal sector of the economy. Generally, the government budget restraint has been ignored, the treatment of tax functions is highly aggregated and all government expenditure variables are treated as exogenous. Furthermore, the estimates in these models have become dated because of major data revisions. On these grounds and also as a part of continuing efforts towards building macroeconometric models for the Indian economy, we have specified, estimated and analysed a new model containing thirty four equations out of which eighteen are stochastic. Its special features are an endogenous treatment of government consumption expenditure, a disaggregated treatment of tax-revenue functions, an endogenous money-supply function and a distinction between the agricultural and non-agricultural sectors in terms of prices, outputs and investments. The model is estimated by mixed estimation procedures. In particular, two stage least squares with subsets of predetermined variables in the first stage and with first order autoregressive corrections in a few cases have been used. The model is used for forecasting and policy simulation. Its forecasting performance, within the sample period, and in a 'pseudo' forecast period is found to be satisfactory compared against 'naive' and 'not-so-naive' extrapolative benchmark models. Various policy simulations have been done and subsequently the model is used for conditional forecasting. We find that increases in government consumption expenditure have detrimental effects on real output, that changes in tax-rates and discount rates have very marginal impact on the system and that important policy changes relate to expenditure variables and government deficit financing.