arXiv subjects

Xiaojun Mao

Publications and source records attributed to Xiaojun Mao.

21 records · Page 2Linked to original sources

arXiv2020

Adjusting for Spatial Effects in Genomic Prediction

This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect phenotypic measurements of plants. We consider a Gaussian random field model with an additive covariance structure that incorporates genotype effects, spatial effects and subpopulation effects. An empirical study shows the existence of spatial effects and heterogeneity across different subpopulation families, while simulations illustrate the improvement in selecting genotypically superior plants by adjusting for spatial effects in genomic prediction.

stat.AP↗

arXiv2020

Matrix Completion under Low-Rank Missing Mechanism

Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study matrix completion from corrupted data under a novel low-rank missing mechanism. The probability matrix of observation is estimated via a high dimensional low-rank matrix estimation procedure, and further used to complete the target matrix via inverse probabilities weighting. Due to both high dimensional and extreme (i.e., very small) nature of the true probability matrix, the effect of inverse probability weighting requires careful study. We derive optimal asymptotic convergence rates of the proposed estimators for both the observation probabilities and the target matrix.

stat.ML↗

arXiv2019

Matrix Completion for Survey Data Prediction with Multivariate Missingness

The National Health and Nutrition Examination Survey (NHANES) studies the nutritional and health status over the whole U.S. population with comprehensive physical examinations and questionnaires. However, survey data analyses become challenging due to inevitable missingness in almost all variables. In this paper, we develop a new imputation method to deal with multivariate missingness at random using matrix completion. In contrast to existing imputation schemes either conducting row-wise or column-wise imputation, we treat the data matrix as a whole which allows exploiting both row and column patterns to impute the missing values in the whole data matrix at one time. We adopt a column-space-decomposition model for the population data matrix with easy-to-obtain demographic data as covariates and a low-rank structured residual matrix. A unique challenge arises due to lack of identification of parameters in the sample data matrix. We propose a projection strategy to uniquely identify the parameters and corresponding penalized estimators, which are computationally efficient and possess desired statistical properties. The simulation study shows that the doubly robust estimator using the proposed matrix completion for imputation has smaller mean squared error than other competitors. To demonstrate practical relevance, we apply the proposed method to the 2015-2016 NHANES Questionnaire Data.

stat.ME↗