Weight smoothing for nonprobability surveys Ferri García, Ramón Nonprobability samples Propensity score adjustment Tree-based inverse propensity-weighted estimator Weight smoothing This study was partially supported by Ministerio de Ciencia e Innovacion, Spain [grant number PID2019-106861RB-I00/AEI/10.13039/501100011033], the IMAG-Maria de Maeztu grant [grant number CEX2020-001105-M/AEI/10.13039/501100011033] and, in terms of the first author, a FPU grant from the Ministerio de Ciencia, Innovacion y Universidades, Spain. Funding for open access charge: Universidad de Granada/CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Adjustment techniques to mitigate selection bias in nonprobability samples often involve modelling the propensity to participate in the nonprobability sample along with inverse propensity weighting. It is well known that procedures for estimating weights are effective if the covariates selected in the propensity model are related to both the variable of interest and the participation indicator. In most surveys, there are many variables of interest, making weight adjustments difficult to determine as a suitable weight for one variable may be unsuitable for other variables. The standard compromise is to include a large number of covariates in the propensity model but this may increase the variability of the estimates, especially when some covariates are weakly related to the variables of interest. Weight smoothing, developed for probability surveys, could be helpful in these situations. It aims to remove the variability caused by overfit propensity models by replacing the inverse propensity weights with predicted weights obtained using a smoothing model. In this article, we study weight smoothing in the nonprobability survey context, both theoretically and empirically, to understand its effectiveness at improving the efficiency of estimates. 2022-01-11T11:50:04Z 2022-01-11T11:50:04Z 2021-12-20 journal article Ferri-García, R... [et al.]. Weight smoothing for nonprobability surveys. TEST (2021). [https://doi.org/10.1007/s11749-021-00795-7] http://hdl.handle.net/10481/72307 10.1007/s11749-021-00795-7 eng http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España Springer