arXiv subjects

Junlong Zhao

Publications and source records attributed to Junlong Zhao.

12 recordsLinked to original sources

arXiv2026

Transfer Learning with Heterogeneous Feature Spaces in Linear Regression

Transfer learning improves target-task performance by leveraging related source data. Most methods assume shared feature spaces, yet in many applications, each source observes only a subset of target covariates. Classical imputation fails here due to block missingness, and standard imputation matrices are not optimized for target parameter estimation. We study low- and high-dimensional linear regression and propose Heterogeneous Importance Weighting (HIW). Our method aligns feature spaces via projection-based imputation and transfers information through sample-selected importance weighting. This framework accommodates diverse projection matrices to construct target-oriented imputation. We develop a classification-based procedure with pseudo-responses to estimate conditional error densities for the weights. We establish entry-wise and global convergence rates for the estimator, with numerical and real-data studies demonstrating its effectiveness.

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arXiv2025

Hub Detection in Gaussian Graphical Models

Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estimating the graphical model, and then using the estimated graph to identify hubs. Despite its simplicity, the success of this strategy relies on the accuracy of the estimated graph. In this paper, we directly target on the estimation of hubs, without the need of estimating the graph. We establish a novel connection between the presence of hubs in a graphical model, and the spectral decomposition of the underlying covariance matrix. Based on this connection, we propose the method of inverse principal components for hub detection (IPC-HD). Both consistency and convergence rates are established for IPC-HD. Our simulation study demonstrates the superior performance and fast computation of the proposed method compared to existing methods in the literature in terms of hub detection. Our application to a prostate cancer gene expression dataset detects several hub genes with close connections to tumor development.

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arXiv2024

Profiled Transfer Learning for High Dimensional Linear Model

We develop here a novel transfer learning methodology called Profiled Transfer Learning (PTL). The method is based on the \textit{approximate-linear} assumption between the source and target parameters. Compared with the commonly assumed \textit{vanishing-difference} assumption and \textit{low-rank} assumption in the literature, the \textit{approximate-linear} assumption is more flexible and less stringent. Specifically, the PTL estimator is constructed by two major steps. Firstly, we regress the response on the transferred feature, leading to the profiled responses. Subsequently, we learn the regression relationship between profiled responses and the covariates on the target data. The final estimator is then assembled based on the \textit{approximate-linear} relationship. To theoretically support the PTL estimator, we derive the non-asymptotic upper bound and minimax lower bound. We find that the PTL estimator is minimax optimal under appropriate regularity conditions. Extensive simulation studies are presented to demonstrate the finite sample performance of the new method. A real data example about sentence prediction is also presented with very encouraging results.

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arXiv2023

Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression

Transfer learning is an emerging paradigm for leveraging multiple sources to improve the statistical inference on a single target. In this paper, we propose a novel approach named residual importance weighted transfer learning (RIW-TL) for high-dimensional linear models built on penalized likelihood. Compared to existing methods such as Trans-Lasso that selects sources in an all-in-all-out manner, RIW-TL includes samples via importance weighting and thus may permit more effective sample use. To determine the weights, remarkably RIW-TL only requires the knowledge of one-dimensional densities dependent on residuals, thus overcoming the curse of dimensionality of having to estimate high-dimensional densities in naive importance weighting. We show that the oracle RIW-TL provides a faster rate than its competitors and develop a cross-fitting procedure to estimate this oracle. We discuss variants of RIW-TL by adopting different choices for residual weighting. The theoretical properties of RIW-TL and its variants are established and compared with those of LASSO and Trans-Lasso. Extensive simulation and a real data analysis confirm its advantages.

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arXiv2023

$n$-photon blockade with an $n$-photon parametric drive

We propose a mechanism to engineer an $n$-photon blockade in a nonlinear cavity with an $n$-photon parametric drive $\lambda(\hat{a}^{\dag n}+\hat{a}^n)$. When an $n$-photon-excitation resonance condition is satisfied, the presence of n photons in the cavity suppresses the absorption of the subsequent photons. To confirm the validity of this proposal, we study the n-photon blockade in an atom-cavity system, a Kerr-nonlinear resonator, and two-coupled Kerr nonlinear resonators. Our results demonstrate that $n$-photon bunching and $(n+1)$-photon antibunching can be simultaneously obtained in these systems. This effect is due both to the anharmonic energy ladder and to the nature of the $n$-photon drive. To show the importance of the drive, we compare the results of the $n$-photon drive with a coherent (one-photon) drive, proving the enhancement of antibunching in the parametric-drive case. This proposal is general and can be applied to realize the $n$-photon blockade in other nonlinear systems.

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arXiv2021

Hypothesis Testing of One-Sample Mean Vector in Distributed Frameworks

Distributed frameworks are widely used to handle massive data, where sample size $n$ is very large, and data are often stored in $k$ different machines. For a random vector $X\in \mathbb{R}^p$ with expectation $μ$, testing the mean vector $H_0: μ=μ_0$ vs $H_1: μ\ne μ_0$ for a given vector $μ_0$ is a basic problem in statistics. The centralized test statistics require heavy communication costs, which can be a burden when $p$ or $k$ is large. To reduce the communication cost, distributed test statistics are proposed in this paper for this problem based on the divide and conquer technique, a commonly used approach for distributed statistical inference. Specifically, we extend two commonly used centralized test statistics to the distributed ones, that apply to low and high dimensional cases, respectively. Comparing the power of centralized test statistics and the distributed ones, it is observed that there is a fundamental tradeoff between communication costs and the powers of the tests. This is quite different from the application of the divide and conquer technique in many other problems such as estimation, where the associated distributed statistics can be as good as the centralized ones. Numerical results confirm the theoretical findings.

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arXiv2020

Angle-based hierarchical classification using exact label embedding

Hierarchical classification problems are commonly seen in practice. However, most existing methods do not fully utilize the hierarchical information among class labels. In this paper, a novel label embedding approach is proposed, which keeps the hierarchy of labels exactly, and reduces the complexity of the hypothesis space significantly. Based on the newly proposed label embedding approach, a new angle-based classifier is developed for hierarchical classification. Moreover, to handle massive data, a new (weighted) linear loss is designed, which has a closed form solution and is computationally efficient. Theoretical properties of the new method are established and intensive numerical comparisons with other methods are conducted. Both simulations and applications in document categorization demonstrate the advantages of the proposed method.

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arXiv2019

New Error Analysis for Lasso

The Lasso is one of the most important approaches for parameter estimation and variable selection in high dimensional linear regression. At the heart of its success is the attractive rate of convergence result even when $p$, the dimension of the problem, is much larger than the sample size $n$. In particular, Bickel et al. (2009) showed that this rate, in terms of the $\ell_1$ norm, is of the order $s\sqrt{(\log p)/n}$ for a sparsity index $s$. In this paper, we obtain a new bound on the convergence rate by taking advantage of the distributional information of the model. Under the normality or sub-Gaussian assumption, the rate can be improved to nearly $s/\sqrt{n}$ for certain design matrices. We further outline a general partitioning technique that helps to derive sharper convergence rate for the Lasso. The result is applicable to many covariance matrices suitable for high-dimensional data analysis.

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arXiv2017

Multiple Influential Point Detection in High-Dimensional Spaces

Influence diagnosis is an integrated component of data analysis, but is severely under-investigated in a high-dimensional setting. One of the key challenges, even in a fixed-dimensional setting, is how to deal with multiple influential points giving rise to the masking and swamping effects. This paper proposes a novel group deletion procedure referred to as MIP by studying two extreme statistics based on a marginal correlation based influence measure. Named the Min and Max statistics, they have complimentary properties in that the Max statistic is effective for overcoming the masking effect while the Min statistic is useful for overcoming the swamping effect. Combining their strengths, we further propose an efficient algorithm that can detect influential points with a prespecified false discovery rate. The proposed influential point detection procedure is simple to implement, efficient to run, and enjoys attractive theoretical properties. Its effectiveness is verified empirically via extensive simulation study and data analysis. An R package implementing the procedure is freely available.

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arXiv2016

An analysis of penalized interaction models

An important consideration for variable selection in interaction models is to design an appropriate penalty that respects hierarchy of the importance of the variables. A common theme is to include an interaction term only after the corresponding main effects are present. In this paper, we study several recently proposed approaches and present a unified analysis on the convergence rate for a class of estimators, when the design satisfies the restricted eigenvalue condition. In particular, we show that with probability tending to one, the resulting estimates have a rate of convergence $s\sqrt{\log p_1/n}$ in the $\ell_1$ error, where $p_1$ is the ambient dimension, $s$ is the true dimension and $n$ is the sample size. We give a new proof that the restricted eigenvalue condition holds with high probability, when the variables in the main effects and the errors follow sub-Gaussian distributions. Under this setup, the interactions no longer follow Gaussian or sub-Gaussian distributions even if the main effects follow Gaussian, and thus existing works are not applicable. This result is of independent interest.

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arXiv2014

Estimation for ultra-high dimensional factor model: a pivotal variable detection based approach

For factor model, the involved covariance matrix often has no row sparse structure because the common factors may lead some variables to strongly associate with many others. Under the ultra-high dimensional paradigm, this feature causes existing methods for sparse covariance matrix in the literature not directly applicable. In this paper, for general covariance matrix, a novel approach to detect these variables that is called the pivotal variables is suggested. Then, two-stage estimation procedures are proposed to handle ultra-high dimensionality in factor model. In these procedures, pivotal variable detection is performed as a screening step and then existing approaches are applied to refine the working model. The estimation efficiency can be promoted under weaker assumptions on the model structure. Simulations are conducted to examine the performance of the new method and a real dataset is analysed for illustration.

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arXiv2013

High-dimensional influence measure

Influence diagnosis is important since presence of influential observations could lead to distorted analysis and misleading interpretations. For high-dimensional data, it is particularly so, as the increased dimensionality and complexity may amplify both the chance of an observation being influential, and its potential impact on the analysis. In this article, we propose a novel high-dimensional influence measure for regressions with the number of predictors far exceeding the sample size. Our proposal can be viewed as a high-dimensional counterpart to the classical Cook's distance. However, whereas the Cook's distance quantifies the individual observation's influence on the least squares regression coefficient estimate, our new diagnosis measure captures the influence on the marginal correlations, which in turn exerts serious influence on downstream analysis including coefficient estimation, variable selection and screening. Moreover, we establish the asymptotic distribution of the proposed influence measure by letting the predictor dimension go to infinity. Availability of this asymptotic distribution leads to a principled rule to determine the critical value for influential observation detection. Both simulations and real data analysis demonstrate usefulness of the new influence diagnosis measure.

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