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Sparse Design Problem in Local Linear Quasi-likelihood Estimator

국소선형 준가능도 추정량의 자료 희박성 문제 해결방안

  • 박동련 (한신대학교 정보통계학과)
  • Published : 2007.03.31

Abstract

Local linear estimator has a number of advantages over the traditional kernel estimators. The better performance near boundaries is one of them. However, local linear estimator can produce erratic result in sparse regions in the realization of the design and to solve this problem much research has been done. Local linear quasi-likelihood estimator has many common properties with local linear estimator, and it turns out that sparse design can also lead local linear quasi-likelihood estimator to erratic behavior in practice. Several methods to solve this problem are proposed and their finite sample properties are compared by the simulation study.

국소선형 추정량은 여러 면에서 바람직한 특성을 많이 갖고 있는 좋은 추정량이다. 그러나 자료가 희박한 부분에서는 매우 불안정한 추정값을 갖게 되는 문제가 있음이 밝혀졌으며, 이 문제를 해결하기 위한 여러 방안이 많이 연구되었다. 그러나 이항반응변수를 위한 국소선형 추정량의 변형이라고 할 수 있는 국소선형 준가능도 추정량에 대해서는 아직 자료의 희박성 문제가 다루어지지 않고 있었다. 이 논문에서는 국소선형 준가능도 추정량이 갖고 있는 자료의 희박성 문제를 인식하고, 몇 가지 해결방안을 제시하였으며, 모의 실험을 통하여 가장 효과적인 방안을 선택하였다.

Keywords

References

  1. Fan, J. and Chen, J. (1999). One-step local quasi-likelihood estimation, Journal of the Royal Statistical Society, Ser. B, 61, 927-943 https://doi.org/10.1111/1467-9868.00211
  2. Fan, J., Heckman, N. E. and Wand, M. P. (1995). Local polynomial kernel regression for generalized linear models and quasi-likelihood functions, Journal of the American Statistical Association, 90, 141-150 https://doi.org/10.2307/2291137
  3. Hall, P. and Turlach, B. A. (1997). Interpolation method for adapting to sparse design in nonparametric regression, Journal of the American Statistical Association, 92, 466-477 https://doi.org/10.2307/2965694
  4. Jennen-Steinmetz, C. and Gasser, T. (1988). A unifying approach to nonparametric regression estimation, Journal of the American Statistical Association, 83, 1084-1089 https://doi.org/10.2307/2290140
  5. Muller, H. and Schmitt, T. (1988). Kernel and probit estimates in quantal bioassay, Journal of the American Statistical Association, 83, 750-759 https://doi.org/10.2307/2289301
  6. Park, D. (1999). Comparison of two response curve estimators, Journal of Statistical Computation and Simulation, 62, 259-269 https://doi.org/10.1080/00949659908811935
  7. Park, D. and Park, S. (2006). Parametric and nonparametric estimators of ED100$\alpha$, Journal of Statistical Computation and Simulation, 76, 661-672 https://doi.org/10.1080/10629360500279706
  8. Seifert, B. and Gasser, T. (1996). Finite-sample variance of local polynomials: Analysis and solutions, Journal of the American Statistical Association, 91, 267-275 https://doi.org/10.2307/2291404