S-Space College of Natural Sciences (자연과학대학) Dept. of Statistics (통계학과) Theses (Master's Degree_통계학과)
Generalized Partially linear models with missing data
결측 데이터를 포함한 일반화 부분 선형모형과 응용
- 자연과학대학 통계학과
- Issue Date
- 서울대학교 대학원
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2014. 8. 박병욱.
- In multivariate data with binary outcome, having some of explanatory variable missing, generalized linear models are sometimes hard to be applied. Among the covariates, some behave linearly and the others do nonlinearly when having effect on the outcome variable, which is the variable to be expected statistically. In this case, the generalized partially linear models, which considers the aspects of both generalized linear model and generalized additive model, is one of the ways to deal with. Especially, under the existence of missing data, WEE(weighted estimating equation) method can be employed in order to estimate the function of interest.
It is interesting that the probability of missing covariates does not affect the biasedness of the estimator. This is because the inverse probability weighting method gives weights to another data of near values of missing, to complement missing portion. Also, we apply the kernel regression to estimate nonparametric term. Since the bandwidth h is necessary, we also check that how critical bandwidth selection is by making several simulation and computing biasedness of estimates.