Ma, Chung Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 56-57). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Additive model --- p.3 / Chapter 1.2 --- Multiplicative model --- p.4 / Chapter 2 --- Review of benchmarking methods --- p.7 / Chapter 2.1 --- Regression Method and Signal Extraction Method (Additive Case) --- p.7 / Chapter 2.1.1 --- Regression method for Additive model --- p.8 / Chapter 2.1.2 --- Signal extraction method with known auto-covariance of signal for Additive model --- p.9 / Chapter 2.1.3 --- Signal extraction method with unknown auto-covariance of signal for Additive model --- p.10 / Chapter 2.2 --- Regression Method and Signal Extraction Method (Multiplicative Case) --- p.15 / Chapter 2.2.1 --- Regression method for Multiplicative model --- p.15 / Chapter 2.2.2 --- Signal extraction method for Multiplicative model --- p.17 / Chapter 3 --- Benchmarking using an assigned error model --- p.21 / Chapter 3.1 --- Model setting --- p.22 / Chapter 3.2 --- Simulation procedures --- p.24 / Chapter 3.3 --- Simulation results --- p.26 / Chapter 3.3.1 --- Comparison of benchmarking methods for multiplicative model --- p.34 / Chapter 3.3.2 --- Suggestions on the selection of an assigned error model --- p.39 / Chapter 3.4 --- Summary on the effects of CV and Φ --- p.44 / Chapter 4 --- Error modelling by using benchmarks --- p.47 / Chapter 4.1 --- Benchmark Forecasting Method --- p.47 / Chapter 4.2 --- Benchmarking using an estimated error model --- p.49 / Chapter 4.3 --- Comparison of the best Φ and the estimated Φ on the benchmarking prediction --- p.53 / Chapter 5 --- Conclusion --- p.54 / Bibliography --- p.56

This thesis introduces a new algorithm to search for optimal spatial sampling design. It is found in previous studies of Zhu and Stein (2006) that the optimal sampling design for spatial prediction with estimated parameters is nearly regular with a few clustering points. The pattern is similar to the generalized Neyman-Scott (GNS) process introduced by Yau and Loh (2012), which allows for regularity in the parent process. This motivates the use of a realization of the GNS process as a spatial sampling design. This method translates the high dimensional optimization problem of selecting sampling sites into a low dimensional optimization problem of searching for the optimal parameter sets in the GNS process. Simulation studies indicate that the proposed sampling design algorithm is more computationally efficient while the result of criterion minimization is comparable to traditional methods. / 本文介紹了一種新的算法來搜索最優空間採樣設計。先前Zhu和Stein（2006）的研究發現，按被估計參數的空間預測的最優採樣設計是近乎有規律的，同時伴隋一些聚類點。該圖案與Yau和Loh（2012）介紹的廣義Neyman-Scott（GNS）過程相似，其中的父過程擁有規律性。這驅使我們使用GNS過程的實現作為空間採樣設計。這種方法把選擇採樣點的高維優化問題轉化為搜索最優GNS過程參數集的低維優化問題。模擬實驗顯示，該採樣設計的算法是計算效率更高，同時其最小化判別函數的結果是可以媲美傳統的方法。 / Lai, Sai Yu. / Thesis M.Phil. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 31-34). / Abstracts also in Chinese. / Title from PDF title page (viewed on 18, October, 2016). / Detailed summary in vernacular field only.

Cheung, Ka Wai Ricker. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 68-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Basic Concepts --- p.1 / Chapter 1.2 --- Linkage Disequilibrium --- p.5 / Chapter 1.3 --- Transmission Disequilibrium Test --- p.7 / Chapter 1.4 --- Scope of Thesis --- p.8 / Chapter 2 --- Transmission Disequilibrium Test --- p.9 / Chapter 2.1 --- The Model --- p.9 / Chapter 2.2 --- The Data Structure and The Statistic --- p.12 / Chapter 3 --- Small Sample Properties of Transmission Disequilibrium Test --- p.16 / Chapter 3.1 --- Exact Distribution of TDT Statistic --- p.16 / Chapter 3.2 --- Power under Alternative Hypothesis --- p.20 / Chapter 3.3 --- P-Value --- p.29 / Chapter 4 --- Exact P-Value and Power --- p.35 / Chapter 5 --- Haplotype Relative Risk --- p.61 / Chapter 6 --- Conclusion --- p.66 / References --- p.68

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This dissertation concerns two topics in the analysis of finite population surveys: setting sample size and hypothesis testing. The first concerns the a priori determination of the sample size needed to obtain species members. The second concerns testing distributional hypotheses when two equal-size populations are sampled. Setting sample size to obtain species is a problem which arises when an investigator wants to obtain (1) a member of all species present in an area (2) a member of all species whose relative frequency is greater than, say, 20% or (3) a member of each species in a target set of species. Chapter 2 presents a practical solution to these questions by setting a target sample size for which the species are obtained with known probability. The solution requires the estimated relative frequency of the rarest species of interest; total number of species is not needed. Because this problem has substantial computational demands, easy-to-compute formulas are needed and given. Three practical examples are presented. Testing of finite population distributional hypotheses is covered in Chapter 3. The test proposed here works under reasonably general designs and is based on a Horvitz-Thompson type correction of the usual Mann-Whitney U statistic. The investigation here compared this proposed test to a corrected (for finiteness) form of the usual Wilcoxon rank sum test. Size and power of the two test procedures are investigated using simulation. The proposed test had approximately correct nominal size over a wide range of situations. The corrected Wilcoxon test exhibited extreme violations in size in many cases. Power of the two tests in situations where they have equal size is similar in most practically interesting cases. / Graduation date: 1996

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xi, 116 p. : ill. (some col.), col. maps. Vita. Includes abstract. Includes bibliographical references (p. 55-56).

Not available / text

published_or_final_version / abstract / toc / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy