arXiv subjects

Yin Xia

Publications and source records attributed to Yin Xia.

At least 37 records · Page 2Linked to original sources

arXiv2023

NAPA: Neighborhood-Assisted and Posterior-Adjusted Two-sample Inference

Two-sample multiple testing problems of sparse spatial data are frequently arising in a variety of scientific applications. In this article, we develop a novel neighborhood-assisted and posterior-adjusted (NAPA) approach to incorporate both the spatial smoothness and sparsity type side information to improve the power of the test while controlling the false discovery of multiple testing. We translate the side information into a set of weights to adjust the $p$-values, where the spatial pattern is encoded by the ordering of the locations, and the sparsity structure is encoded by a set of auxiliary covariates. We establish the theoretical properties of the proposed test, including the guaranteed power improvement over some state-of-the-art alternative tests, and the asymptotic false discovery control. We demonstrate the efficacy of the test through intensive simulations and two neuroimaging applications.

stat.ME↗

arXiv2023

Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models

This paper presents a selective survey of recent developments in statistical inference and multiple testing for high-dimensional regression models, including linear and logistic regression. We examine the construction of confidence intervals and hypothesis tests for various low-dimensional objectives such as regression coefficients and linear and quadratic functionals. The key technique is to generate debiased and desparsified estimators for the targeted low-dimensional objectives and estimate their uncertainty. In addition to covering the motivations for and intuitions behind these statistical methods, we also discuss their optimality and adaptivity in the context of high-dimensional inference. In addition, we review the recent development of statistical inference based on multiple regression models and the advancement of large-scale multiple testing for high-dimensional regression. The R package SIHR has implemented some of the high-dimensional inference methods discussed in this paper.

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arXiv2021

Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

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arXiv2021

A Normality Test for High-dimensional Data based on a Nearest Neighbor Approach

Many statistical methodologies for high-dimensional data assume the population is normal. Although a few multivariate normality tests have been proposed, to the best of our knowledge, none of them can properly control the type I error when the dimension is larger than the number of observations. In this work, we propose a novel nonparametric test that utilizes the nearest neighbor information. The proposed method guarantees the asymptotic type I error control under the high-dimensional setting. Simulation studies verify the empirical size performance of the proposed test when the dimension grows with the sample size and at the same time exhibit a superior power performance of the new test compared with alternative methods. We also illustrate our approach through two popularly used data sets in high-dimensional classification and clustering literatures where deviation from the normality assumption may lead to invalid conclusions.

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arXiv2020

Individual Data Protected Integrative Regression Analysis of High-dimensional Heterogeneous Data

Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high dimensional setting. The challenge is even more pronounced when the individual level data cannot be shared across studies, known as DataSHIELD constraint (Wolfson et al., 2010). Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Theoretically, SHIR is statistically more efficient than the existing distributed approaches that integrate debiased LASSO estimators from the local sites. Furthermore, the estimation error incurred by aggregating derived data is negligible compared to the statistical minimax rate and SHIR is shown to be asymptotically equivalent in estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of our method is studied and compared with existing approaches via extensive simulation settings. We further illustrate the utility of SHIR to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple chronic disease cohorts.

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arXiv2020

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensional setting often fails due to the limited sample size. One approach to improve power is through meta-analyzing multiple studies on the same scientific question. However, integrative analysis of high dimensional data from multiple studies is challenging in the presence of between study heterogeneity. The challenge is even more pronounced with additional data sharing constraints under which only summary data but not individual level data can be shared across different sites. In this paper, we propose a novel data shielding integrative large-scale testing (DSILT) approach to signal detection by allowing between study heterogeneity and not requiring sharing of individual level data. Assuming the underlying high dimensional regression models of the data differ across studies yet share similar support, the DSILT approach incorporates proper integrative estimation and debiasing procedures to construct test statistics for the overall effects of specific covariates. We also develop a multiple testing procedure to identify significant effects while controlling for false discovery rate (FDR) and false discovery proportion (FDP). Theoretical comparisons of the DSILT procedure with the ideal individual--level meta--analysis (ILMA) approach and other distributed inference methods are investigated. Simulation studies demonstrate that the DSILT procedure performs well in both false discovery control and attaining power. The proposed method is applied to a real example on detecting interaction effect of the genetic variants for statins and obesity on the risk for Type 2 Diabetes.

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arXiv2019

Nucleon spin polarization in intermediate-energy heavy-ion collisions

Based on a spin-dependent Boltzmann-Uehling-Uhlenbeck transport model with spin-dependent potentials incorporated using the lattice Hamiltonian method, we have studied the global spin polarization perpendicular to the reaction plane as well as the local spin polarization in the longitudinal direction in non-central intermediate-energy heavy-ion collisions, as an extension of similar studies in relativistic heavy-ion collisions. Both the global and the local spin polarizations are found to be mostly dominated by the time-odd component of the nuclear spin-orbit potential. Impacts of various theoretical uncertainties on the nucleon spin polarization as well as its dependence on the beam energy and impact parameter are discussed. Our study serves as a baseline for understanding the spin polarization mechanism of strongly interacting matter produced in heavy-ion collisions dominated by nucleon degree of freedom.

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arXiv2019

Hypothesis Testing for Network Data with Power Enhancement

Comparing two population means of network data is of paramount importance in a wide range of scientific applications. Many existing network inference solutions focus on global testing of entire networks, without comparing individual network links. Besides, the observed data often take the form of vectors or matrices, and the problem is formulated as comparing two covariance or precision matrices under a normal or matrix normal distribution. Moreover, many tests suffer from a limited power under a small sample size. In this article, we tackle the problem of network comparison, both global and simultaneous inferences, when the data come in a different format, i.e., in the form of a collection of symmetric matrices, each of which encodes the network structure of an individual subject. Such data format commonly arises in applications such as brain connectivity analysis and clinical genomics. We no longer require the underlying data to follow a normal distribution, but instead impose some moment conditions that are easily satisfied for numerous types of network data. Furthermore, we propose a power enhancement procedure, and show that it can control the false discovery, while it has the potential to substantially enhance the power of the test. We investigate the efficacy of our testing procedure through both an asymptotic analysis and a simulation study under a finite sample size. We further illustrate our method with an example of brain structural connectivity analysis.

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arXiv2019

Paired Test of Matrix Graphs and Brain Connectivity Analysis

Inferring brain connectivity network and quantifying the significance of interactions between brain regions are of paramount importance in neuroscience. Although there have recently emerged some tests for graph inference based on independent samples, there is no readily available solution to test the change of brain network for paired and correlated samples. In this article, we develop a paired test of matrix graphs to infer brain connectivity network when the groups of samples are correlated. The proposed test statistic is both bias corrected and variance corrected, and achieves a small estimation error rate. The subsequent multiple testing procedure built on this test statistic is guaranteed to asymptotically control the false discovery rate at the pre-specified level. Both the methodology and theory of the new test are considerably different from the two independent samples framework, owing to the strong correlations of measurements on the same subjects before and after the stimulus activity. We illustrate the efficacy of our proposal through simulations and an analysis of an Alzheimer's Disease Neuroimaing Initiative dataset.

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arXiv2018

Fictitious GAN: Training GANs with Historical Models

Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game between two neural networks. Here, we leverage this game theoretic view to study the convergence behavior of the training process. Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced. Fictitious GAN trains the deep neural networks using a mixture of historical models. Specifically, the discriminator (resp. generator) is updated according to the best-response to the mixture outputs from a sequence of previously trained generators (resp. discriminators). It is shown that Fictitious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach. It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.

cs.LG↗

arXiv2017

Simulating spin dynamics with spin-dependent cross sections in heavy-ion collisions

We have incorporated the spin-dependent nucleon-nucleon cross sections into a Boltzmann-Uehling-Uhlenbeck transport model for the first time, using the spin-singlet and spin-triplet nucleon-nucleon elastic scattering cross sections extracted from the phase-shift analyses of nucleon-nucleon scatterings in free space. We found that the spin splitting of the collective flows is not affected by the spin-dependent cross sections, justifying it as a good probe of the in-medium nuclear spin-orbit interaction. With the in-medium nuclear spin-orbit mean-field potential that leads to local spin polarization, we found that the spin-averaged observables, such as elliptic flows of free nucleons and light clusters, becomes smaller with the spin-dependent differential nucleon-nucleon scattering cross sections.

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arXiv2016

Spin transport in intermediate-energy heavy-ion collisions as a probe of in-medium spin-orbit interactions

The spin up-down splitting of collective flows in intermediate-energy heavy-ion collisions as a result of the nuclear spin-orbit interaction is investigated within a spin- and isospin-dependent Boltzmann-Uehing-Uhlenbeck transport model SIBUU12. Using a Skyrme-type spin-orbit coupling quadratic in momentum, we found that the spin splittings of the directed flow and elliptic flow are largest in peripheral Au+Au collisions at beam energies of about 100-200 MeV/nucleon, and the effect is considerable even in smaller systems especially for nucleons with high transverse momenta. The collective flows of light clusters of different spin states are also investigated using an improved dynamical coalescence model with spin. Our study can be important in understanding the properties of in-medium nuclear spin-orbit interactions once the spin-dependent observables proposed in this work can be measured.

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arXiv2016

Equations of motion of test particles for solving the spin-dependent Boltzmann-Vlasov equation

A consistent derivation of the equations of motion (EOMs) of test particles for solving the spin-dependent Boltzmann-Vlasov equation is presented. The resulting EOMs in phase space are similar to the canonical equations in Hamiltonian dynamics, and the EOM of spin is the same as that in the Heisenburg picture of quantum mechanics. Considering further the quantum nature of spin and choosing the direction of total angular momentum in heavy-ion reactions as a reference of measuring nucleon spin, the EOMs of spin-up and spin-down nucleons are given separately. The key elements affecting the spin dynamics in heavy-ion collisions are identified. The resulting EOMs provide a solid foundation for using the test-particle approach in studying spin dynamics in heavy-ion collisions at intermediate energies. Future comparisons of model simulations with experimental data will help constrain the poorly known in-medium nucleon spin-orbit coupling relevant for understanding properties of rare isotopes and their astrophysical impacts.

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arXiv2015

Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis

Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is characterized by a graph, where nodes represent neural elements such as neurons and brain regions, and links represent statistical dependences that are often encoded in terms of partial correlations. Such a graph is inferred from matrix-valued neuroimaging data such as electroencephalography and functional magnetic resonance imaging. There have been a good number of successful proposals for sparse precision matrix estimation under normal or matrix normal distribution; however, this family of solutions do not offer a statistical significance quantification for the estimated links. In this article, we adopt a matrix normal distribution framework and formulate the brain connectivity analysis as a precision matrix hypothesis testing problem. Based on the separable spatial-temporal dependence structure, we develop oracle and data-driven procedures to test the global hypothesis that all spatial locations are conditionally independent, which are shown to be particularly powerful against the sparse alternatives. In addition, simultaneous tests for identifying conditional dependent spatial locations with false discovery rate control are proposed in both oracle and data-driven settings. Theoretical results show that the data-driven procedures perform asymptotically as well as the oracle procedures and enjoy certain optimality properties. The empirical finite-sample performance of the proposed tests is studied via simulations, and the new tests are applied on a real electroencephalography data analysis.

stat.ME↗

arXiv2015

Dynamical effects of spin-dependent interactions in low- and intermediate-energy heavy-ion reactions

It is well known that non-central nuclear forces, such as the spin-orbital coupling and the tensor force, play important roles in understanding many interesting features of nuclear structures. However, their dynamical effects in nuclear reactions are poorly known since only the spin-averaged observables are normally studied both experimentally and theoretically. Realizing that spin-sensitive observables in nuclear reactions may carry useful information about the in-medium properties of non-central nuclear interactions, besides earlier studies using the time-dependent Hartree-Fock approach to understand effects of spin-orbital coupling on the threshold energy and spin polarization in fusion reactions, some efforts have been made recently to explore dynamical effects of non-central nuclear forces in intermediate-energy heavy-ion collisions using transport models. The focuses of these studies have been on investigating signatures of the density and isospin dependence of the form factor in the spin-dependent single-nucleon potential. Interestingly, some useful probes were identified in the model studies while so far there is still no data to compare with. In this brief review, we summarize the main physics motivations as well as the recent progress in understanding the spin dynamics and identifying spin-sensitive observables in heavy-ion reactions at intermediate energies. We hope the interesting, important, and new physics potentials identified in the spin dynamics of heavy-ion collisions will stimulate more experimental work in this direction.

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arXiv2015

Revisit of the neutron/proton ratio puzzle in intermediate-energy heavy-ion collisions

Incorporating a newly improved isospin- and momentum-dependent interaction in the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model IBUU11, we have investigated relative effects of the density dependence of nuclear symmetry energy $E_{sym}(ρ)$ and the neutron-proton effective mass splitting $m^*_n-m^*_p$ on the neutron/proton ratio of free nucleons and those in light clusters. It is found that the $m^*_n-m^*_p$ has a relatively stronger effect than the $E_{sym}(ρ)$ and the assumption of $m^*_n\leq m^*_p$ leads to a higher neutron/proton ratio. Moreover, this finding is independent of the in-medium nucleon-nucleon cross sections used. However, results of our calculations using the $E_{sym}(ρ)$ and $m^*_n-m^*_p$ both within their current uncertainty ranges are all too low compared to the recent NSCL/MSU double neutron/proton ratio data from central $^{124}$Sn+$^{124}$Sn and $^{112}$Sn+$^{112}$Sn collisions at 50 and 120 MeV/u, thus calling for new mechanisms to explain the puzzlingly high neutron/proton ratio observed in the experiments.

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arXiv2014

Spin-orbit coupling and the up-down differential transverse flow in intermediate-energy heavy-ion collisions

To explore the strength, isospin dependence, and density dependence of the in-medium spin-orbit coupling that is poorly known but relevant for understanding the structure of rare isotopes, the nucleon spin up-down differential transverse flow is studied systematically by varying the beam energy and impact parameter of Au+Au collisions within a spin-isospin dependent transport model recently developed for investigating spin-related phenomena in intermediate-energy heavy-ion collisions. The optimal reaction conditions for delineating and disentangling effects of different terms in the spin-orbit coupling are discussed.

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arXiv2014

Cross-view Action Modeling, Learning and Recognition

Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel multiview spatio-temporal AND-OR graph (MST-AOG) representation for cross-view action recognition, i.e., the recognition is performed on the video from an unknown and unseen view. As a compositional model, MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. This paper proposes effective methods to learn the structure and parameters of MST-AOG. The inference based on MST-AOG enables action recognition from novel views. The training of MST-AOG takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, which is error-prone and time-consuming, but the recognition does not need 3D information and is based on 2D video input. A new Multiview Action3D dataset has been created and will be released. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition on 2D videos.

cs.CV↗