Nonparametric multivariate boundary-bias-free density estimation and its application

2016 Fiscal Year Final Research Report

• Project/Area Number: 15H06068
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Single-year Grants
• Research Field: Economic statistics
• Research Institution: University of Tsukuba
• Principal Investigator: IGARASHI Gaku 筑波大学, システム情報系, 助教 (40759346)
• Project Period (FY): 2015-08-28 – 2017-03-31
• Keywords: ノンパラメトリック密度推定

Outline of Final Research Achievements

We focused on nonparametric density estimation when the support of the estimated density has the boundary. We proposed multivariate log-normal kernel density estimator and its generalization, using the idea of the weighted distribution, and derived their asymptotic properties. We also proposed inverse gamma and Amoroso kernel density estimators, and derived their asymptotic properties. Furthermore, we discussed the bias-reduced beta and Amoroso kernel density estimators. Simulation studies and data analyses were conducted to illustrate the asymptotic results.

Free Research Field

統計科学