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Several reports research the best prediction power of the credit risk models for different industries. The structural models use firm¡¦s information for firms¡¦ structural variables, such as asset value and asset volatility, to determine the time of default, but it suffer from some drawbacks, which represent the main reasons behind their relatively poor empirical performance. It require estimates for the parameters of the firm¡¦s asset value, which is nonobservable. Moody's KMV model is well known and useful among them, but it ignores recovery rate and difference in financial structure and industry. The reduced-form models fundamentally differ from typical structural models in the degree of predictability of the default. Reduced-form models use market data and assume the probability of default is exogenously generated. However, the basel committee for banking supervision proposed that risk is endogenous. The purpose of this paper is using quantile and threshold regression to introduce a new approach which is based on the Moody¡¦s KMV model, the Lu and Kuo ( 2005) and the Altman, Brooks Brady, Resti and Sironi (2005) to the evaluation of the endogenous probability of default and the endogenous recovery rate.

This research focuses on characterizing a potato with extra sugar content and identifying the location and depth of the extra sugar content using the active thermography imaging technique. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The inspection system and general methodologies characterizing extra sugar content will be presented in this study. The average heating rate obtained from the thermal image analysis is the major factor in characterization procedures. Using information on the average heating rate, the probability of achieving a potato with extra sugar content may be predicted using the logistic regression model. In addition, neural networks are also used to identify the potato with extra sugar contents. The correct rate for identifying a potato with extra sugar content in it can reach 85%. The location of extra sugar content can also be found using the logistic regression model. Results show the overall correct rate predicting the extra sugar content location with a resolution of 20 by 20 pixels is 91%. In predicting the extra sugar content depth, amounts exceeds 2/3 inches are not detectable by analyzing thermal images. The depth of extra sugar content can be discriminated in 0.3 inch increments with a high rate of accuracy (87.5%).

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Taiwan is usually considered as a small open economy. Trade and exchange rate policies in Taiwan have substantially changed since the mid-1980s. Not only has trade been liberalized, but exchange rates of the New Taiwan Dollar(NTD) were also allowed to fluctuate. This paper applies the Threshold Regression Model that puted forward of Cancer and Hansen (2004) and combines the expectation-augmented Phillips curve with a threshold for the pass-through. The paper examines whether the short-run magnitude of the pass-through is affected by the business cycle, direction and magnitude of the exchange rate change. For that purpose, two variables are tested as thresholds: (1)output gap, (2)exchange rate change. The results indicate that the short-run pass-through is higher when the economy is booming, as well as the exchange rate depreciates above some threshold. And they have important implications for monetary policy and are possibly related to pricing-to-market behavior and menu costs of price a djustment.

Globalization in business has become increasingly common, so stronger company that is seeking for growth tends to merge and acquire other companies since it is more beneficial to take over an existing firm's operations and niche compared to expanding on its own. Integration shortly after merger and acquisition is key to a company¡¦s long-term success, because top-management team must alter an organization¡¦s structure and establish new strategies to adjust to the rapid changing environment. Therefore, we can conclude that the role of top-management teams during the process of mergers and acquisitions is critical. After merger and acquisition, whether or not the top-management team of the target firm should be replaced remains a good question. Not only should the working ability and the accomplishment of the top-management teams be considered, but other internal and external reasons that might affect this alteration should also be considered. This research looks to discuss why the top-management team for the target firm should or should not remain in the office. We¡¦ve selected acquisition firm and target firm from the listed companies at the stock exchange market, over-the-counter market, and emerging stock market in 1997 to 2006 to be our study sample. We will try to figure out the retained ratio for the top management teams by using regression model analysis. The result of this study shows that the type of M & A, the experience of the acquisition firm, and the type of industry they are in have great impact on the retained ratio of the top-management teams.

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Master of Science / Department of Statistics / Weixing Song / There has been a continuing interest among statisticians in the problem of regression models wherein the independent variables are measured with error and there is considerable literature on the subject. In the following report, we discuss the errors-in-variables regression model: yi = β0 + β1xi + β2zi + ϵi,Xi = xi + ui,Zi = zi + vi with i.i.d. errors (ϵi, ui, vi), for i = 1, 2, ..., n and find the least square estimators for the parameters of interest. Both weak and strong consistency for the least square estimators βˆ0, βˆ1, and βˆ2 of the unknown parameters β0, β1, and β2 are obtained. Moreover, under regularity conditions, the asymptotic normalities of the estimators are reported.

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.