Nonparametric Inference via Bootstrapping the Debiased Estimator

In this paper, we propose to construct confidence bands by bootstrapping the debiased kernel density estimator (for density estimation) and the debiased local polynomial regression estimator (for regression analysis). The idea of using a debiased estimator was recently employed by Calonico et al. (2018b) to construct a confidence interval of the density function (and regression function) at a given point by explicitly estimating stochastic variations. We extend their ideas of using the debiased estimator and further propose a bootstrap approach for constructing simultaneous confidence bands. This modified method has an advantage that we can easily choose the smoothing bandwidth from conventional bandwidth selectors and the confidence band will be asymptotically valid. We prove the validity of the bootstrap confidence band and generalize it to density level sets and inverse regression problems. Simulation studies confirm the validity of the proposed confidence bands/sets. We apply our approach to an Astronomy dataset to show its applicabilityComment: Accepted to the Electronic Journal of Statistics. 64 pages, 6 tables, 11 figure

Reducing variance in univariate smoothing

A variance reduction technique in nonparametric smoothing is proposed: at each point of estimation, form a linear combination of a preliminary estimator evaluated at nearby points with the coefficients specified so that the asymptotic bias remains unchanged. The nearby points are chosen to maximize the variance reduction. We study in detail the case of univariate local linear regression. While the new estimator retains many advantages of the local linear estimator, it has appealing asymptotic relative efficiencies. Bandwidth selection rules are available by a simple constant factor adjustment of those for local linear estimation. A simulation study indicates that the finite sample relative efficiency often matches the asymptotic relative efficiency for moderate sample sizes. This technique is very general and has a wide range of applications.Comment: Published at http://dx.doi.org/10.1214/009053606000001398 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Adolescent precursors of early union formation among Asian American and Whites

This study investigates the relatively low rates of early marriage and cohabitation among Asian Americans compared to Whites. With an emphasis on family value socialization and other precursors measured in adolescence, data from Waves 1 and 3 of Add Health are used to test five hypotheses. Analyses of early marriage indicate that the Asian-White difference is driven primarily by differences in adolescent sexual and romantic relationship experiences, and several measures of family values play a stronger role among Asian Americans than Whites. Asian-White differences in cohabitation persist net of SES and other adolescent precursors, but differences are attenuated when parental value socialization, intimate relationship experiences, and educational investments are controlled. These results are interpreted within a culturally sensitive conceptual framework that emphasizes independent versus interdependent construals of the self.America