Authors: Zhicheng Chen
Data loss is a big problem in many online monitoring systems due to various reasons. Copula-based approaches are effective imputation methods for missing data imputation; however, such methods are highly dependent on a reliable distribution of missing data. This article proposed a functional regression approach for missing probability density function (PDF) imputation. PDFs are first transformed to a Hilbert space by the log quantile density (LQD) transformation. The transformed results of the response PDFs are approximated by the truncated Karhunen–Loève representation. Corresponding representation in the Hilbert space of a missing PDF is estimated by a vector-on-function regression model in reproducing kernel Hilbert space (RKHS), then mapping back to the density space by the inverse LQD transformation to obtain an imputation for the missing PDF. To address errors caused by the numerical integration in the inverse LQD transformation, original PDFs are aided by a PDF of uniform distribution. The effect of the added uniform distribution in the imputed result of a missing PDF can be separated by the warping function-based PDF estimation technique.
Comments: 6 Pages.
[v1] 2017-07-24 15:57:44
Unique-IP document downloads: 41 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.