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Master of Science / Department of Statistics / Weixin Yao / In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimate, LTS estimate, S-estimate, [tau]-estimate, MM-estimate, GM-estimate, and REWLS estimate. Finally, we compare these robust estimates based on their robustness and efficiency through a simulation study. A real data set application is also provided to compare the robust estimates with traditional least squares estimator.

Some highway agencies in the United States are experiencing frequent distresses in asphalt pavements on bridge approaches/departures. Commonly observed distresses include alligator cracking and rutting, which reduce roadway smoothness and safety. To lessen the distresses in pavements it is needed to investigate the extent and root causes of the problem. Based on Florida highway conditions, this research study mainly focused on1. Literature review and identification of the extent of the problem; 2. Collection of relevant pavement condition data and descriptive analysis; 3. Development of statistical models to determine factors influencing the distresses in asphalt pavements on bridge approaches/departures. To the best of my knowledge, this is the first study that uses a statistical model to determine the factors that are responsible for causing asphalt pavement distresses on bridge approaches/departures. As part of the literature review, a nationwide questionnaire survey was targeted towards U.S state DOTs. The data collection and analysis specific to the Florida highways found that in 2015 on Florida Interstate highways, about 27% bridges with asphalt pavements on their approaches/departures showed signs of cracking, and about 20% bridges have noticeable rutting in their approach or departure pavements. A random parameter linear regression model was applied to examine the factors that may influence distresses in asphalt pavements in Florida. Pavement condition was evaluated based on the Florida Department of Transportation (FDOT) 2015 pavement condition data and video log images, and other relevant data were collected from various sources such as FDOT Roadway Characteristics Inventory (RCI) database, FDOT pavement management reports, and FDOT Ground Penetrating Radar (GPR) survey reports. A constraint existed in the availability of the GPR data that can give pavement layer thickness, which limited the number of bridge approach pavement sections included in the statistical modeling. Based on the limited data, the estimated results from the random parameter linear regression model showed that the variables influencing distresses in asphalt pavements on bridge approaches/departures, in terms of rutting and roughness, may include pavement age, annual average daily truck traffic, and surface friction course.

Mobile personal computing devices continue to proliferate and individuals' reliance on them for day-to-day needs necessitate that these platforms be secure. Mobile computers are subject to a unique form of denial of service attack known as a battery exhaustion attack, in which an attacker attempts to rapidly drain the battery of the device. Battery exhaustion attacks greatly reduce the utility of the mobile devices by decreasing battery life. If steps are not taken to thwart these attacks, they have the potential to become as widespread as the attacks that are currently mounted against desktop systems. This thesis presents steps in the design of an intrusion detection system for detecting these attacks, a system that takes into account the performance, energy, and memory constraints of mobile computing devices. This intrusion detection system uses several parameters, such as CPU load and disk accesses, to estimate the power consumption of two test systems using multiple linear regression models, allowing us to find the energy used on a per process basis, and thus identifying processes that are potentially battery exhaustion attacks. / Master of Science

Community and Regional Planning / In the past two decades, Austin has tremendous population growth, job opportunity in the downtown core and transportation challenges associated with that. Public transit, and particularly rail, often is regarded as a strategy to help reduce urban traffic congestion. The Urban Rail, which combines features of streetcars and light rail, is introduced into Austin as a new transit rail. The City of Austin, Capital Metro and Lone Star Rail are actively studying routing, financial, environmental and community elements associated with a first phase of Urban Rail. This thesis collected 2010 Origin and Destination Rail Transit Survey data from Capital Metropolitan Transportation Authority. The research focuses on the rail transit ridership. Two regression models are applied to analyze the factors influencing Austin rail transit ridership. One model is focusing on the socioeconomic characteristics. One model is focusing on the spatial factors. Our model shows that demographic factors have more significant effect than spatial factors. In addition, this work also tries to analyze the correlations between those factors and make recommendations based on the analysis result. / text

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The price of real estate is the result of economical operation and, most importantly, regulation mechanism of resource distribution for real estate industry. Since the process of economic reform began in 1978, there have been several times that the Chinese government imposed contractive measures intended to slow down the economic growth. This paper applies insights from economic theory to explain recent housing price patterns in China’s four largest metropolitan areas. (Beijing, Shanghai, Shenzhen and Guangzhou) and discusses how the Chinese Government’s stance and policy affect the development of real estate. By examining the degree of impact on the housing market as a result of Macro Control Measures, excluding other housing market drivers; the empirical results revealed the degree of effectiveness by the Chinese Government administrative control over the housing market vary across the regions.

This thesis deals with the appropriate handling of spatial data in general, and in particular in the framework of economic sciences. An overview of well known methods from the field of spatial statistics and spatial econometrics is given. Furthermore a special class of spatial objects is described, namely objects that are that far apart from all other observations in the dataset, that they are not connected to them anymore. Different treatments of such objects are suggested and their influence on the Moran's I test for spatial autocorrelation is analyzed in more detail. After this theoretical part some adequate spatial methods are applied to the well-known problem of R&D spillovers. The corresponding dataset is not obviously spatial, nevertheless spatial methods can be used. The spatial contiguity matrix is based on an economic distance measure instead of the commonly used geographic distances. Finally, optimal design theory and spatial analysis are combined via a new criterion. This criterion was developed to be able to take a potential spatial dependency of the data points into account. The aim is to find the best design points that show the same spatial dependence structure as the true population. (author's abstract)

The goal of the thesis „Econometric model of flat prices in Brno“ is to create econometric model based on data from housing market. The theoretical part of the thesis defines variables, and use descriptive statistics. The practical part of the thesis deals with creation econometric model and interactive calculator.