1 volume

No description available.

Marburg, Universiẗat, Diss., 2003. / Erscheinungsjahr an der Haupttitelstelle: 2002.

Köln, University, Diss., 2004.

轉換點(change point)問題出現在許多的統計領域，包括了有母數、無母數、迴歸、時間序列、序貫、貝式等模型，本研究主要是針對簡單迴歸模型的估計單一轉換點之問題作探討，若我們均勻分布解釋變數並採用最小平方法來估計轉換點，模擬結果告訴我們估計值會有雙峰的現象，此現象造成了變異數的增大。我們嘗試利用二階段設計來改善之前的估計，藉由第一階段所得到轉換點的可能範圍來估計第二階段的實驗，模擬結果顯示此兩階段估計的確降低了估計值的差異。 / The change point problem can be involved in many models such as parametric, nonparametric, regression, time series, sequential, Bayesian, and so on. This thesis focuses on estimating the location of the change point in a simple regression model. We first show that the computational simulation demonstrates a bimodal phenomenon which could increase the variation of the estimator badly when the independent variables are allocated uniformly over the explanatory interval and the least square method is used to determine the estimator of the change point. In the second part,we implement a two-stage design that tries to shrink the possible location of the change point via first stage and then design the second stage accordingly. Simulation result gives a positive response in reducing the variation caused by the bimodal phenomenon.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 253-257). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 253-257). Also available on the Internet.

Lai Kim Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 55-57). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Likelihood Ratio Test Statistic --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- Formulation --- p.6 / Chapter 2.3 --- Likelihood Ratio Type Statistic --- p.7 / Chapter 2.4 --- Dirichlet Distribution --- p.8 / Chapter 2.5 --- Edgeworth Expansion --- p.12 / Chapter 3 --- Divided Difference --- p.15 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.2 --- Definition of Divided Difference --- p.15 / Chapter 3.3 --- Theorem --- p.17 / Chapter 3.4 --- Proof of the Theorem --- p.18 / Chapter 3.5 --- Application of Theorem --- p.19 / Chapter 4 --- Computational Results --- p.22 / Chapter 4.1 --- Introduction --- p.22 / Chapter 4.2 --- Critical Values for Moderate and Large Sample Sizes --- p.22 / Chapter 4.3 --- Critical Values for Small Sample Sizes --- p.23 / Chapter 4.3.1 --- Exact Critical Values --- p.23 / Chapter 4.3.2 --- Edgeworth Expansion Results --- p.23 / Chapter 4.3.3 --- Simulation Results --- p.23 / Chapter 4.4 --- Power --- p.24 / Chapter 5 --- Illustrative Examples --- p.29 / Chapter 5.1 --- Stanford Heart Transplant Data --- p.29 / Chapter 5.1.1 --- The Data --- p.29 / Chapter 5.1.2 --- Result --- p.31 / Chapter 5.2 --- Air Conditioning Data --- p.31 / Chapter 5.2.1 --- The Data --- p.31 / Chapter 5.2.2 --- Result --- p.32 / Chapter 5.3 --- Insulating Fluid Failure Data --- p.33 / Chapter 5.3.1 --- The Data --- p.33 / Chapter 5.3.2 --- Result --- p.33 / Chapter 6 --- Conclusion and Further Research Topic --- p.35 / Chapter 6.1 --- Conclusion --- p.35 / Chapter 6.2 --- Further Research Topic --- p.38 / Appendix A --- p.39 / Appendix B --- p.46 / Bibliography --- p.55

Open site measurement on the electromagnetic interference is the most direct and universally accepted standard approach for measuring radiated emissions from an equipment or the radiation susceptibility of a component or equipment. In general, if the NSA measurements we recorded at different frequencies do not exceed the ideal value +-4dB, we would regard this site as a normalized site, otherwise it is not a normalized site as long as there is one measurement exceeds the range. A one change point model had been used to fit observed measurements. For each set of observations as well as the corresponding ideal values, we have the estimated regression parameter for a one change point model. Our ideal is using the difference of regression parameters between ideal values and observations to assess whether a site is qualified for measuring EMI or not. The assessment tool for whether the testing site is normalized or not is referred to the confidence region for the regression model parameters. Finally, according to the data collected in this experiment, the estimated parameters obtained from the observations will be used to do further statistical analyses and comparing the qualities of the four different testing sites.

Computer integrated manufacturing environments and competition among companies to meet customer requirements raise the need for the use of online methodologies in combination with traditional Statistical Process Control tools. This study focuses on detecting the change point, when a shift in mean occurs, in a normal bivariate process using two different approaches. First, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially Weighted Moving Average (MEWMA) statistical procedures were used in detecting the mean shift in the process. Then the step-change detection and neural network approaches were applied to the outputs of MCUSUM and MEWMA statistical procedures to identify the time of the change. The results show that the step-change and neural network approaches are capable of detecting the time of the change earlier than either the MCUSUM or MEWMA statistical procedure.