http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Paul Nobin Chandra (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Paul Nobin Chandra,Rai Anil,Ahmad Tauqueer,Biswas Ankur,Sahoo Prachi Misra 한국통계학회 2024 Journal of the Korean Statistical Society Vol.53 No.1
A major goal of survey sampling is finite population inference. In recent years, large-scale survey programs have encountered many practical challenges which include higher data collection cost, increasing non-response rate, increasing demand for disaggregated level statistics and desire for timely estimates. Data integration is a new field of research that provides a timely solution to these above-mentioned chal- lenges by integrating data from multiple surveys. Now, it is possible to develop a framework that can efficiently combine information from several surveys to obtain more precise estimates of population parameters. In many surveys, parameters of interest are often spatial in nature, which means, the relationship between the study variable and covariates varies across all locations in the study area and this situation is referred as spatial non-stationarity. Hence, there is a need of a sampling method- ology that can efficiently tackle this spatial non-stationarity problem and can be able to integrate this spatially referenced data to get more detailed information. In this study, a Geographically Weighted Spatially Integrated (GWSI) estimator of finite population total was developed by integrating data from two independent surveys using spatial information. The statistical properties of the proposed spatially inte- grated estimator were then evaluated empirically through a spatial simulation study. Three different spatial populations were generated having high spatial autocorrela- tion. The proposed spatially integrated estimator performed better than usual design- based estimator under all three populations. Furthermore, a Spatial Proportionate Bootstrap (SPB) method was developed for variance estimation of the proposed spa- tially integrated estimator.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
