Typescript (photocopy). / Digitized by Kansas Correctional Industries

Chan For Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 88-90). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Basic Concepts --- p.6 / Chapter 2.1 --- Binomial vs Inverse Sampling --- p.6 / Chapter 2.2 --- Equivalence / Non-inferiority Test --- p.7 / Chapter 3 --- Testing Procedures --- p.9 / Chapter 3.1 --- The Model --- p.9 / Chapter 3.2 --- Asymptotic Behaviors of the Estimators --- p.10 / Chapter 3.2.1 --- Asymptotic Test Statistic based on Unconditional Maximum Likelihood Estimate --- p.12 / Chapter 3.2.2 --- Asymptotic Test Statistic based on restricted maximum likelihood estimate --- p.13 / Chapter 3.3 --- Conditional Exact Procedures --- p.16 / Chapter 3.3.1 --- Non-test-statistic-based procedure --- p.17 / Chapter 3.3.2 --- Test-statistic-based procedure --- p.17 / Chapter 4 --- Simulation Study --- p.19 / Chapter 4.1 --- Simulation Results - Type I error rate --- p.21 / Chapter 4.1.1 --- Asymptotic Test Statistic based on Unconditional MLE . . --- p.21 / Chapter 4.1.2 --- Asymptotic Test Statistic based on Restricted MLE . . . . --- p.22 / Chapter 4.1.3 --- Non-test-statistic-based Conditional Exact Test --- p.23 / Chapter 4.1.4 --- Test-statistic-based Conditional Exact Test --- p.24 / Chapter 4.2 --- Simulation Results - Power --- p.25 / Chapter 4.2.1 --- Asymptotic Tests - Similarity and Difference between using Unconditional and Restricted MLE --- p.25 / Chapter 4.2.2 --- Conditional Exact Tests - Similarity and Difference be- tween using Non-test-statistic-based and Test-statistic-based Procedures --- p.30 / Chapter 4.2.3 --- Test-statistic-based Conditional Exact Tests - Similarity and Difference between using Unconditional and Restricted MLE --- p.31 / Chapter 5 --- Conclusion --- p.32 / Appendices --- p.36 / Chapter A. --- Simulation Result - Type I error rate --- p.36 / Chapter B. --- Simulation Result - Power value --- p.42 / Bibliography --- p.88

Lee Ka-ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Multiple Test Procedure --- p.7 / Chapter 2.1 --- Hypothesis Test for Individual Centre --- p.8 / Chapter 2.2 --- Multiple Hypothesis Test for Multi-Centre --- p.11 / Chapter 2.2.1 --- Single-step Multiple Test Procedure --- p.11 / Chapter 2.2.2 --- Sequentially Rejective Multiple Test Procedure --- p.12 / Chapter 2.2.3 --- Multiple Test Procedure for Discrete Distribution --- p.14 / Chapter 2.2.4 --- Summary of various Multiple Test Procedures --- p.17 / Chapter 3 --- Simulation Study --- p.19 / Chapter 3.1 --- Comparisons of Sizes --- p.19 / Chapter 3.1.1 --- Based on Asymptotic Approach --- p.22 / Chapter 3.1.2 --- Based on Exact Approach --- p.23 / Chapter 3.1.3 --- Based on Mid-P Approach --- p.25 / Chapter 3.1.4 --- "Comparisons between Asymptotic, Exact and Mid-P Approaches" --- p.26 / Chapter 3.2 --- Comparisons of Power --- p.29 / Chapter 3.2.1 --- Based on Asymptotic Approach --- p.32 / Chapter 3.2.2 --- Based on Exact Approach --- p.33 / Chapter 3.2.3 --- Based on Mid-P Approach --- p.33 / Chapter 3.2.4 --- Asymptotic vs. Exact Approaches --- p.34 / Chapter 3.2.5 --- Exact vs. Mid-P Approaches --- p.34 / Chapter 3.2.6 --- Asymptotic vs. Mid-P Approaches --- p.34 / Chapter 4 --- Illustrative Examples --- p.36 / Chapter 5 --- Conclusions and Discussions --- p.43 / Figures --- p.45 / References --- p.68

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

Thesis (Ph. D.)--State University of New York at Binghamton, Dept. of Mathematical Sciences, 2006. / Includes bibliographical references.

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

In the study, young adolescents’ hypothesis development in a computer-supported and problem-based learning environment was examined in terms of two empirical studies. The first study examined the effect of metacognitive scaffolds to strengthening hypothesis development as well as the influence of hypothesis development in the promotion of young adolescents’ problem solving performance in an ill-structured problem solving environment, Animal Investigator. Data was collected from sixth grade students (N = 172). The findings of the study indicated that participants using metacognitive scaffolds attained significantly higher hypothesis-development performance. Results also revealed that the hypothesis-development performance showed the predictive power of the solution development performance. In the second study, the researcher examined three factors, motivation, metacognition, and prior domain knowledge, as a predictor for children’s hypothesisdevelopment performance in the problem-based learning environment. A hypothesized model was evaluated using structural equation modeling, which is a statistical method of causal relationships. Data were collected from sixth grade students (N = 101) in treatment groups. Two significant factors toward children’s hypothesis-development performance in an ill-structured problem solving environment were determined: Prior domain knowledge and metacognition. Implications and limitations of the present study and issues including the experimental design are discussed.

In this study, the space-time relationship of precipitation fields is examined by testing the Taylor's "frozen field" hypothesis (TH). Specifically, the hypothesis supposes that if a spatio-temporal precipitation field with a stationary covariance Cov(r,tau) in both space r and time tau, moves with a constant velocity v, then the temporal covariance at time lag tau is equal to the spatial covariance at space lag v tau, that is, Cov(0;tau) = Cov(v tau, 0). Of specific interest is whether there is a cut-off or decorrelation time scale for which the TH holds for a given mean flow velocity v. The validity of the TH is tested for precipitation fields using high-resolution gridded NEXRAD radar reflectivity data over southeastern United States by employing two different statistical approaches. The first method is based upon rigorous hypothesis testing while the second is based on a simple correlation analysis, which neglects possible dependencies in the correlation estimates. The data-set has an approximate horizontal resolution of 4 km x 4 km and a temporal resolution of 15 minutes, while the time period of study is 4 days. The results of both statistical methods suggest that the TH might hold for the shortest space and time scales resolved by the data (4 km and 15 minutes), but that it does not hold for longer periods or larger spatial scales. The fidelity of global climate models in accurately simulating seasonal mean precipitation in the tropics is investigated by comparisons with satellite observations. Specifically, six-year long (2000-2005) simulations are performed using a high-resolution (36-km) Weather Research Forecast (WRF) model and the Community Atmosphere Model (CAM) at T85 spatial resolution and the results are compared with satellite observations from the Tropical Rainfall Measuring Mission (TRMM). The primary goal is to study the annual cycle of rainfall over four land regions of the tropics namely, the Indian monsoon, the Amazon, tropical Africa and the North American monsoon. The results indicate that the WRF model systematically underestimates the magnitude of monthly mean rainfall over most Tropical land regions but gets the seasonal timing right. On the other hand, CAM produces rainfall magnitudes that are closer to the observations but the rainfall peak leads or lags the observations by a month or two. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36 km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between the models and the observations.

none

Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. Includes bibliographical references.