Seminar by Prof. Jae-Kwang Kim（Hybrid）
- 【Date&Time】
- 26 September 2024 (Thursday) 14:15 -15:15 (+Q&A)
Admission Free
- 【Place】
- ISM Seminar Room 5 (3rd floor)
Online: https://us06web.zoom.us/j/87118916638
- 【Supported by】
- Risk Analysis Research Center, ISM
- 【Speaker】
- Prof. Jae-Kwang Kim (Iowa State Univ.)
- 【Title】
- Debiased calibration estimation using generalized entropy under selection bias
- 【Abstract】
- Incorporating the auxiliary information into the survey estimation is
a fundamental problem in survey sampling. Calibration weighting is a
popular tool for incorporating the auxiliary information. The
calibration weighting method of Deville and Sarndal (1992) uses a
distance measure between the design weights and the final weights to
solve the optimization problem with calibration constraints.
In this paper, we propose a new framework using generalized entropy as
the objective function for optimization. Design weights are used in
the constraints, rather than in the objective function, to achieve
design consistency. The new calibration framework is attractive as it
is general and can produce more efficient calibration weights than the
classical calibration weights. Furthermore, we identify the optimal
choice of the generalized entropy function that achieves the minimum
variance among the different choices of the generalized entropy
function under the same constraints. Asymptotic properties, such as
design consistency and asymptotic normality, are presented rigorously.
An extension of the proposed method to doubly robust propensity score
estimation will also be presented.
