TY  - CHAP
AB  - Abstract In this chapter we consider the “Regularization of Derivative Expectation Operator” (Rodeo) of Lafferty and Wasserman (2008) and propose a modified Rodeo algorithm for semiparametric single index models (SIMs) in big data environment with many regressors. The method assumes sparsity that many of the regressors are irrelevant. It uses a greedy algorithm, in that, to estimate the semiparametric SIM of Ichimura (1993), all coefficients of the regressors are initially set to start from near zero, then we test iteratively if the derivative of the regression function estimator with respect to each coefficient is significantly different from zero. The basic idea of the modified Rodeo algorithm for SIM (to be called SIM-Rodeo) is to view the local bandwidth selection as a variable selection scheme which amplifies the coefficients for relevant variables while keeping the coefficients of irrelevant variables relatively small or at the initial starting values near zero. For sparse semiparametric SIM, the SIM-Rodeo algorithm is shown to attain consistency in variable selection. In addition, the algorithm is fast to finish the greedy steps. We compare SIM-Rodeo with SIM-Lasso method in Zeng et al. (2012). Our simulation results demonstrate that the proposed SIM-Rodeo method is consistent for variable selection and show that it has smaller integrated mean squared errors (IMSE) than SIM-Lasso.
VL  - 40B
SN  - 978-1-83867-419-9, 978-1-83867-420-5/0731-9053
DO  - 10.1108/S0731-90532019000040B005
UR  - https://doi.org/10.1108/S0731-90532019000040B005
AU  - Chu Jianghao
AU  - Lee Tae-Hwy
AU  - Ullah Aman
PY  - 2019
Y1  - 2019/01/01
TI  - Variable Selection in Sparse Semiparametric Single Index Models
T2  - Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B
T3  - Advances in Econometrics
PB  - Emerald Publishing Limited
SP  - 65
EP  - 88
Y2  - 2024/04/25
ER  -