arXiv subjects

Maozai Tian

Publications and source records attributed to Maozai Tian.

6 recordsLinked to original sources

arXiv2026

Scale-dependent contraction of spatial wet-bulb temperature contrasts in eastern China

Regional wet-bulb temperature means omit the spatial distribution of humid heat. We compare upper-quartile and middle-half days of the monthly regional mean at 121 sites in a specified eastern-China domain. A prespecified multiscale architecture combines Gaussian-weighted semivariances at five bandwidths with equal-month, equal-scale and equal-summer aggregation. The specification was developed on the 2015 and 2022 summers and applied unchanged to 33 other summers in 1991-2025. On high-mean days, the weighted mean squared pairwise WBT contrast was 7.28% lower. This corresponds to a 3.71% reduction after square-root transformation and a 3.97% mean reduction in record-level root-mean-square pairwise differences. Contraction strengthened as the Gaussian graph bandwidth increased, from 2.86% at h = 126 km to 13.27% at h = 2,013 km. Exact Laplacian decompositions localised the broad-scale contrast to daily anomalies opposing the monthly north-south climatological pattern, while anomaly energy changed little. The pattern persisted in 1950-1990. A sparse external station comparison showed concordant broad-scale direction. High regional humid heat was associated with a flatter broad geographic field, a feature that a regional mean cannot identify.

stat.AP↗

arXiv2025

Statistical Inference for High-dimensional Matrix-variate Factor Models with Missing Observations

This paper develops an inferential theory for high-dimensional matrix-variate factor models with missing observations. We propose an easy-to-use all-purpose method that involves two straightforward steps. First, we perform principal component analysis on two re-weighted covariance matrices to obtain the row and column loadings. Second, we utilize these loadings along with the matrix-variate data to derive the factors. We develop an inferential theory that establishes the consistency and the rate of convergence under general conditions and missing patterns. The simulation results demonstrate the adequacy of the asymptotic results in approximating the properties of a finite sample. Finally, we illustrate the application of our method using a real numerical dataset.

stat.ME↗

arXiv2022

Non-iterative Gaussianization

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.

stat.ME↗

arXiv2020

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

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 $o(n^α)$ for some $0<α<1$. 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.

stat.ME↗

arXiv2016

Quantile Regression for General Spatial Panel Data Models with Fixed Effects

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.

stat.ME↗

arXiv2016

Quantile Regression for Partially Linear Varying Coefficient Spatial Autoregressive Models

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.

stat.ME↗