This study investigated the relationship of selected building descriptors, district characteristics, and finance, to the per pupil cost of new school construction. Variables selected were: Financial--assessed value, local effort in facility construction, state and federal funds, and the educational expenditures of the district; Building descriptors--the length of construction time, the percentage of instructional space, and the type of facility; district characteristics--geographic location, urban/rural, and net enrollment. The data were gathered from the West Virginia State Department of Education, the State Tax Department, and various architects who designed the 128 new schools constructed under the West Virginia Better School Buildings Amendment of 1972. Analyses utilized the multiple regression procedure to examine the relationship of the variables to construction costs. The results indicated a significant relationship between per pupil construction cost and three financial variables: (1) the assessed value of the district, (2) the amount of local effort put into facility construction and improvement, and (3) the amount of state funds utilized in the project. None of the district characteristics contributed significantly to the variance and the only descriptive variable to yield a significant contribution was the type of facility being constructed--elementary, secondary, or vocational-technical. The study implied need for the standardization of assessed values in West Virginia, equalization of local fiscal effort in school construction, and legislation to inject additional state money in capital outlay. It was recommended that a funding formula for facility construction and improvement be instituted utilizing money from two sources: a local effort fund, accrued and administered at the state level, to which each county would contribute on an equalized basis; and state appropriations designated on a yearly basis. These funds would eliminate the need for local bond issues and special levies for this purpose. It was recommended that further study be done to clarify the relationship between per pupil cost and federal funds, as well as instructional space. / Ed. D.

Master of Science / Department of Statistics / Paul I. Nelson / The principle of least squares applied to regression models estimates parameters by minimizing the mean of squared residuals. Least squares estimators are optimal under normality but can perform poorly in the presence of outliers. This well known lack of robustness motivated the development of alternatives, such as least median of squares estimators obtained by minimizing the median of squared residuals. This report uses simulation to examine and compare the robustness of least median of squares estimators and least squares estimators of the slope of a regression line through the origin in terms of bias and mean squared error in a variety of conditions containing outliers created by using mixtures of normal and heavy tailed distributions. It is found that least median of squares estimation is almost as good as least squares estimation under normality and can be much better in the presence of outliers.

This paper introduces ideas and methods for testing for structural change in linear regression models and presents how these have been realized in an R package called strucchange. It features tests from the generalized fluctuation test framework as well as from the F test (Chow test) framework. Extending standard significance tests it contains methods to fit, plot and test empirical fluctuation processes (like CUSUM, MOSUM and estimates-based processes) on the one hand and to compute, plot and test sequences of F statistics with the supF, aveF and expF test on the other. Thus, it makes powerful tools available to display information about structural changes in regression relationships and to assess their significance. Furthermore it is described how incoming data can be monitored online. / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer better power against certain alternatives, improved size in finite samples for dynamic models and ease of computation respectively. We apply our methods to two data sets, German M1 money demand and U.S. labor productivity. / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The thesis describes the theory of capital asset pricing model (CAPM) and the issue of robust estimates. Robust methods are an effective tool to achieve better estimation relative to the classical least squares method when there is a fai- lure to assume a normal distribution of errors or in the presence of outlying obser- vations in the data. Theory of M-estimates, which is then applied in the practical part of the thesis to the multidimensional CAPM model is treated in detail. The- ory of R- and L-estimates is explained in less detail. A simulation study compares simultaneous estimates in multivariate model and estimates designed individually when applied to the model assuming the mutual independence of equations. 1

The global climate has been affected adversely due to the emissions of large amount of greenhouse gases (GHGs) and there is an urgent need for emissions reduction. Carbon dioxide is a major greenhouse gas and the reduction of its emissions is indispensable to the achievement of sustainable development. The building sector is a major source of carbon dioxide emissions and one way to reduce the emissions is uplifting the energy efficiency of existing buildings by conducting sustainable building refurbishment. As cost would affect the decisions on sustainable building refurbishment strategies, it is necessary to examine the cost of various sustainable refurbishment solutions for existing buildings. Unfortunately, most of the previous studies focused on the technical side with little effort being directed to cost side. Therefore, cost analysis of sustainable building refurbishment solutions should be conducted to fill the research gap. The aim of the research is to develop a cost estimation model to facilitate decision makers to evaluate the cost of sustainable building refurbishment solutions for residential buildings in Hong Kong by considering various factors affecting the cost. The major research methods include literature review, interview and questionnaire survey. Through literature review, the current practice of sustainable building refurbishment is investigated. A variety of refurbishment solutions including improvement on building envelope, renovation of building services system and the use of renewable energy are reviewed. Cost evaluation techniques commonly used in the construction industry including qualitative techniques, quantitative techniques and intelligent methods are also identified and discussed. Due to the limitations of cost data and previous experience, it is suggested that fuzzy logic theory be adopted to develop the cost estimation model. To facilitate the development of the model, the factors affecting the cost of sustainable building refurbishment solutions are examined through literature review and interview. It is found that market price, complexity, efficiency, economy of scale and disturbance are the dominant factors affecting the fluctuation of cost. Finally, a fuzzy reasoning cost estimation model of sustainable building refurbishment solutions is developed. The model is built based on fuzzy reasoning system. Users can input the information of the project and the model is able to evaluate the cost fluctuation based on the information provided by the users. The model is then validated. The cost estimation model developed in this research provides a new way for the construction industry practitioners to estimate the cost of sustainable building refurbishment projects effectively and efficiently. / published_or_final_version / Civil Engineering / Master / Master of Philosophy

There exist a number of well-known and validated design metrics, and the fault prediction available through these metrics has been well documented for systems developed in languages such as C and Ada. However, the mapping and application of these metrics to SDL systems has not been thoroughly explored. The aim of this project is to test the applicability of these metrics in classifying components for testing purposes in a large-scale SDL system. A new model has been developed for this purpose. This research was conducted using a number of SDL systems, most notably actual production models provided by Motorola Corporation. / Department of Computer Science

This paper reviews tests for structural change in linear regression models from the generalized fluctuation test framework as well as from the F test (Chow test) framework. It introduces a unified approach for implementing these tests and presents how these ideas have been realized in an R package called strucchange. Enhancing the standard significance test approach the package contains methods to fit, plot and test empirical fluctuation processes (like CUSUM, MOSUM and estimates-based processes) and to compute, plot and test sequences of F statistics with the supF, aveF and expF test. Thus, it makes powerful tools available to display information about structural changes in regression relationships and to assess their significance. Furthermore, it is described how incoming data can be monitored.

M.Ing. (Engineering Management) / Capital intensive engineering projects involve the flow of large sums of money over the project life. During the pretender phase of the project, estimates of the forecast cash flow and associate return on investment are made based on certain assumptions which at the time are, at best, only informed guesses. As the project progresses, the uncertainty surrounding the magnitude and the timing of these cash flows and rate of return, diminishes. By recognising the time value of money and the importance of the synthesis of time and cash flow as well as the interdependence of project activities, it is desirable to obtain an estimate of the PROBABLE outcome of the return on investment~ should the bid be accepted. This outcome can only be 'guessed' at by means of a probabilistic analysis of the parameters that go to make up the nett cash flow. This study is an attempt to find a model suitable for use in the construction industry which accurately describes the construction process, and presents an overall analysis of the variation in the rate of return as a result of the probabilistic nature of the original parameters. Various models were investigated. All were found suitable under limited conditions.

The aim of this thesis is to analyze the properties of robust estimates and to compare these estimates with regard to the properties between them. The analysis of properties depends on the type of a variable (continuous or discrete), its probability distribution, the range of random sample and the proportion of outliers in random sample. Comparing the properties in different situations will give "guidance" to determine which of the estimates is preferable in specific situations, and which of them should be rather avoided. An adjusted bootstrap method is used to obtain the estimates of properties estimates. The thesis is devided into two parts. In the first part, the parameter estimates, type and design of robust estimators and the bootstrap method are monitored. In the second practical part we determine the suitability of bootstrap to obtain estimates of the properties of robust estimators, followed by obtaining estimates of the properties of the estimates and compare them. At the conclusion of the practical part we observe and compare the values of bootstrap confidence intervals on real data on household income. The results of this thesis shows us, that the bootstrap method does not provide good estimates of the properties of robust estimators in all cases. The results also bring us to the conclusion that from a certain extent of random sample regardless of the number of outliers, you can choose from a robust estimate only on the basis of its value, properties of robust estimates are very similar. Contemplated robust estimates of variability are not suitable estimates in most cases.