[Back to Results | New Search]

URN etd-0626108-135013 Author Pei-Hsin Chou Author's Email Address No Public. Statistics This thesis had been viewed 5068 times. Download 0 times. Department Applied Mathematics Year 2007 Semester 2 Degree Master Type of Document Language zh-TW.Big5 Chinese Title Statistical Inference Date of Defense 2008-05-30 Page Count 152 Keyword finite population correction factor statistical hypothesis type I error composite hypothesis power Central limit theorem Basu theorem critical value goodness of fit test likelihood ratio test Lehmann-Scheffe theorem complete statistic uniformly minimum variance unbiased estimator ancillary statistic P-value type II error Rao-Blackwell theorem simple hypothesis one-sided test two-sided test significance level sufficient statistic alternative hypothesis testing hypothesis null hypothesis rejection region confidence interval confidence level minimum variance unbiased estimator interval estimation uniformly most powerful test Cramer-Rao inequality likelihood function mean square error moment estimator error of estimation point estimation maximum likelihood estimator bias consistent estimator minimal sufficient statistic factorization theorem unbiased estimator statistic most powerful test test statistics Neyman-Pearson lemma decision rule Abstract In this paper, we will investigate the important properties of three major parts of statistical inference: point estimation, interval estimation and hypothesis testing. For point estimation, we consider the two methods of finding estimators: moment estimators and maximum likelihood estimators, and three methods of evaluating estimators: mean squared error, best unbiased estimators and sufficiency and unbiasedness. For interval estimation, we consider the the general confidence interval, confidence interval in one sample, confidence interval in two samples, sample sizes and finite population correction factors. In hypothesis testing, we consider the theory of testing of hypotheses, testing in one sample, testing in two samples, and the three methods of finding tests: uniformly most powerful test, likelihood ratio test and goodness of fit test. Many examples are used to illustrate their applications. Advisory Committee Mong-Na Lo Huang - chair

Mei-Hui Guo - co-chair

Fu-Chuen Chang - advisor

Files indicate not accessible

etd-0626108-135013.pdf Date of Submission 2008-06-26