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320 results on '"Tian, Maozai"'

6. Non-iterative Gaussianization

Author

Rui, Rongxiang and Tian, Maozai

Subjects
Statistics - Methodology, Statistics - Other Statistics
Abstract

In this work, we propose a non-iterative Gaussian transformation strategy based on copula function, which doesn't require some commonly seen restrictive assumptions in the previous studies such as the elliptically symmetric distribution assumption and the linear independent component analysis assumption. Theoretical properties guarantee the proposed strategy can exactly transfer any random variable vector with a continuous multivariate distribution to a variable vector that follows a multivariate Gaussian distribution. Simulation studies also demonstrate the outperformance of such a strategy compared to some other methods like Box-Cox Gaussianization and radial Gaussianization. An application for probability density estimation for image synthesis is also shown., Comment: 26 pages, 5 figures

Published
2022

10. Communication-Efficient Distributed Estimator for Generalized Linear Models with a Diverging Number of Covariates

Author

Zhou, Ping, Yu, Zhen, Ma, Jingyi, Tian, Maozai, and Fan, Ye

Subjects
Statistics - Methodology, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Distributed statistical inference has recently attracted immense attention. The asymptotic efficiency of the maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation estimator are established for generalized linear models under the "large $n$, diverging $p_n$" framework, where the dimension of the covariates $p_n$ grows to infinity at a polynomial rate $o(n^\alpha)$ for some $0<\alpha<1$. Then a novel method is proposed to obtain an asymptotically efficient estimator for large-scale distributed data by two rounds of communication. In this novel method, the assumption on the number of servers is more relaxed and thus practical for real-world applications. Simulations and a case study demonstrate the satisfactory finite-sample performance of the proposed estimators.

Published
2020

16. Quantile Regression for Partially Linear Varying Coefficient Spatial Autoregressive Models

Author

Dai, Xiaowen, Li, Shaoyang, and Tian, Maozai

Subjects
Statistics - Methodology
Abstract

This paper considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed for the approximation of varying coefficients. The instrumental variable quantile regression approach is employed for parameter estimation. The rank score tests are developed for hypotheses on the coefficients, including the hypotheses on the non-varying coefficients and the constancy of the varying coefficients. The asymptotic properties of the proposed estimators and test statistics are both established. Monte Carlo simulations are conducted to study the finite sample performance of the proposed method. Analysis of a real data example is presented for illustration., Comment: 27 pages, 4 figures

Published
2016

17. Quantile Regression for General Spatial Panel Data Models with Fixed Effects

Author

Dai, Xiaowen, Yan, Zhen, Tian, Maozai, and Tang, Manlai

Subjects
Statistics - Methodology
Abstract

This paper considers the quantile regression model with both individual fixed effect and time period effect for general spatial panel data. Instrumental variable quantile regression estimators will be proposed. Asymptotic properties of the proposed estimators will be developed. Simulations are conducted to study the performance of the proposed method. We will illustrate our methodologies using a cigarettes demand data set., Comment: 22 pages, 1 figure

Published
2016

19. Imputed mean tensor regression for near-sited spatial temporal data.

Author

Liang, Jinwen and Tian, Maozai

Subjects
*MULTIPLE imputation (Statistics), *MISSING data (Statistics), *SENSOR networks, *DATA analysis
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Case clustering, contact stratification, and transmission heterogeneity of SARS-CoV-2 Omicron BA.5 variants in Urumqi, China: An observational study

Author

Lu, Yaoqin, primary, Guo, Zihao, additional, Zeng, Ting, additional, Sun, Shengzhi, additional, Lu, Yanmei, additional, Teng, Zhidong, additional, Tian, Maozai, additional, Wang, Jun, additional, Li, Shulin, additional, Fan, Xucheng, additional, Luan, Zemin, additional, Wang, Weiming, additional, Cai, Yongli, additional, Wang, Kai, additional, and Zhao, Shi, additional

Published
2023
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