Flexible generalized varying coefficient regression models

By Young K. Lee, Enno Mammen, Byeong U. Park. 2012

52 citations recorded by the source corpus.

In this paper we introduce new estimators of the coefficient functions in the varying coefficient regression model. The proposed estimators are obtained by projecting the vector of the full-dimensional kernel-weighted local polynomial estimators of the coefficient functions onto a Hilbert space with a suitable norm. We provide a backfitting algorithm to compute the estimators. We show that the algorithm converges at a geometric rate under weak conditions. We derive the asymptotic distributions of the estimators and show that the estimators have the oracle properties. This is done for the general order of local polynomial fitting and for the estimation of the derivatives of the coefficient functions, as well as the coefficient functions themselves. The estimators turn out to have several theoretical and numerical advantages over the marginal integration estimators studied by Yang, Park, Xue and Hardle [J. Amer. Statist. Assoc. 101 (2006) 1212–1227].

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2a7eb8ca1aca50681e78a1352fc5b8f7906eb73b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Varying Coefficient Regression Models: A Review and New Developments (2015, methodology): “The most general varying coefficient model for sieve estimation was treated in Lee et al. (2012b).”