Pseudo Likelihood Ratio Confidence Intervals for the Mean of a Population Containing Many Zero Values under Unequal Probability Sampling
碩士 === 淡江大學 === 數學學系碩士班 === 97 === The many-zero-observation data in survey sampling under complex probability sampling is considered. The confidence interval derived using the traditional normal approximation based on the central limit theorem (CLT) has poor coverage rate even when the sample size...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2009
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| Online Access: | http://ndltd.ncl.edu.tw/handle/96633296887057125771 |
| Summary: | 碩士 === 淡江大學 === 數學學系碩士班 === 97 === The many-zero-observation data in survey sampling under complex probability sampling is considered. The confidence interval derived using the traditional normal approximation based on the central limit theorem (CLT) has poor coverage rate even when the sample size is large. The maximum likelihood method (ML) of Kvanli et al (1998) does not work well either. This paper expands the pseudo likelihood method of Chen and Sitter (1999) that integrated unequal probability sampling design (Cochran, 1977). The method utilizes auxiliary information which has a correlation coefficient ρ with the data. A comparison between different methods was conducted under distinct ρ values and nonzero proportion α, and various nonzero distributions. Simulation results indicate that the pseudo likelihood method improves the coverage rate substantially and outperforms the CLT and ML methods.
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