Imputed quantile vector autoregressive model for multivariate spatial–temporal data

Abstract

AbstractImputing missing values in multivariate spatial–temporal data is important in many fields. Existing low rank tensor learning methods are popular for handling this task but are sensitive to high level of skewness. The aim of this paper is to develop an alternative method with robustness and high imputation accuracy for multivariate spatial–temporal data. In view of the fact that quantile regression is robust to noises and outliers, we propose an imputed quantile vector autoregressive (IQVAR) model. IQVAR can simultaneously impute missing values and estimate parameters of quantile vector autoregressive model. The objective function includes check loss and nuclear norm penalization. We develop an ADMM (Alternating Direction Method of Multipliers) algorithm to solve the resulting optimization problem. Simulation studies and real data analysis are conducted to verify the efficiency of IQVAR. Compared with other approaches, IQVAR is more robust and accurate.