This dissertation is a collection of three stand-alone research papers. Thereby, the class of local polynomial matching estimators is the central object of investigation. The first essay concentrates on applying local polynomial matching methods in order to account for missing data when...

In order to guarantee confidentiality and privacy of firm-level data, statistical offices apply various disclosure limitation techniques. However, each anonymization technique has its protection limits, such that the probability of disclosing the individual information for some observations is...

This paper addresses the choice of an optimal smoothing parameter for local polynomial matching. A version of Empirical Bias Bandwidth Selection (EBBS) proposed by Ruppert (1997) is applied to account for the MSE computation of the matching estimator. Thereby, an estimator for the large sample...

This paper addresses the choice of an optimal smoothing parameter for local polynomial matching estimators. In order to estimate the MSE of the matching estimator as a bandwidth selection criterion, the Double Smoothing approach (Mýller (1985), Hýrdle, Hall, and Marron (1992)) is applied. The...

Statistical disclosure limitation is widely used by data collecting institutions to provide safe individual data. In this paper, we propose to combine two separate disclosure limitation techniques blanking and addition of independent noise in order to protect the original data. The proposed...