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Instrumental-Variable Calibration Estimation in Survey Sampling
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 전종우 | - |
dc.contributor.author | 박승환 | - |
dc.date.accessioned | 2017-07-14T00:30:53Z | - |
dc.date.available | 2017-07-14T00:30:53Z | - |
dc.date.issued | 2013-08 | - |
dc.identifier.other | 000000012862 | - |
dc.identifier.uri | https://hdl.handle.net/10371/121143 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2013. 8. 전종우. | - |
dc.description.abstract | The prediction model, which makes effective use of auxiliary information available throughout the population, is often used to derive efficient estimation in survey sampling.
To protect from the failure of the assumed model, asymptotic design unbiasedness is often imposed in the prediction estimator. An instrumental-variable calibration estimator can be considered to achieve the model optimality among the class of calibration estimators that is asymptotically design unbiased. In this paper, we propose a new calibration estimator that is asymptotically equivalent to the optimal instrumental-variable calibration estimator. The resulting weights are no smaller than one and can be constructed to achieve the range restrictions. The proposed method can be extended to calibration estimation under two-phase sampling. Some numerical results are presented using the real data example of the 1997 National Resource Inventory of the United States. | - |
dc.description.tableofcontents | Contents
1 Introduction 1 1.1. Outline of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Calibration Estimator 7 2.1. Minimizing Distance Function Approach . . . . . . . . . . . . 8 2.2. Functional Form Approach . . . . . . . . . . . . . . . . . . . . 11 2.3. Calibration in Two Phase Sampling . . . . . . . . . . . . . . . 15 3 Prediction Estimation 18 4 The Proposed Method 23 4.1. The Proposed Calibration Estimator . . . . . . . . . . . . . . 23 4.2. Variance Estimation . . . . . . . . . . . . . . . . . . . . . . . 36 4.3. Calibration for Two-Phase Sampling . . . . . . . . . . . . . . 41 5 Simulation Study 50 5.1. Study 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2. Study 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 6 Real Data Example 61 7 Concluding Remarks 65 Appendix 67 A.1 Denition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 A.2 Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 A.3 NRI Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Bibliography 73 Abstract (in Korean) 78 감사의 글 79 | - |
dc.format | application/pdf | - |
dc.format.extent | 1468400 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Asymptotic design unbiasedness | - |
dc.subject | Exponential tilting | - |
dc.subject | Regression estimation | - |
dc.subject | Weighting | - |
dc.subject.ddc | 519 | - |
dc.title | Instrumental-Variable Calibration Estimation in Survey Sampling | - |
dc.type | Thesis | - |
dc.description.degree | Doctor | - |
dc.citation.pages | iii,80 | - |
dc.contributor.affiliation | 자연과학대학 통계학과 | - |
dc.date.awarded | 2013-08 | - |
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