Can I read Imputed Mean Tensor Regression for Near-sited Spatial Temporal Data on EtoBox?

Imputed Mean Tensor Regression for Near-sited Spatial Temporal Data by Jinwen Liang; Maozai Tian is a Mathematics article available to read on EtoBox.

What is Imputed Mean Tensor Regression for Near-sited Spatial Temporal Data about?

Modern spatial temporal data are often collected from sensor networks. Missing data problems are common to this kind of data. Making accurate imputation is important for many applications. In the unsupervised setting, one technique is to minimize the rank of a tensor or matrix. If we add related covariates, can we get more accurate imputation results? To address this, we transform the original sensor×time measurements to high order tensors by adding additional temporal dimensions and then integrate tensor regression with tensor completion using nuclear norm penalty. One advantage is we can simultaneously estimate parameters and impute missing values due to clear spatial consistency for near-sited spatial-temporal data. The proposed method doesn't assume missing mechanism of the response. Theoretical properties of the proposed estimator are investigated. Simulation studies and real data analysis are conducted to verify the efficiency of the estimation procedure.

Who reads Imputed Mean Tensor Regression for Near-sited Spatial Temporal Data?

Author

Jinwen Liang; Maozai Tian

Published

2023

Language

EN

Field

Mathematics (Physical Sciences)