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Jing Ouyang

Publications and source records attributed to Jing Ouyang.

17 recordsLinked to original sources
arXiv2026

Endogeneity-Aware Cognitive Diagnostic Model for Multidomain Ordinal Assessments

Multidomain assessment batteries generate ordinal item responses that are often summarized through latent attribute profiles. Conventional cognitive diagnostic models (CDMs) provide interpretable measurement models for such profiles, but they typically do not represent directed dependence among latent attributes from distinct domains. We propose an endogeneity-aware cognitive diagnostic model (EACDM) for multivariate ordinal assessments. The model combines a block-structured diagnostic measurement component, in which item groups are linked to domain-specific binary attributes through a block-diagonal Q-matrix, with a logistic structural component, in which one attribute block is regressed on another block and subject-level covariates while accounting for latent classification uncertainty. This formulation yields a parsimonious framework for studying endogenous relationships among diagnostic attributes without collapsing domain-specific measurement structure. We establish identifiability conditions for the Q-matrix, effective loadings, latent-profile probabilities, and structural coefficients, and develop a Markov chain Monte Carlo algorithm for joint estimation of the measurement and structural components. Simulation studies demonstrate accurate recovery of item parameters, latent structures, and structural coefficients for the proposed EACDM, whereas conventional CDMs can fail to recover the ground truth when endogeneity is present. We apply the proposed method to Parkinson's disease data to examine how non-motor latent traits relate to motor impairment profiles.

stat.ME↗
arXiv2026

Contrastive Mask Fidelity: Reference-Free Auditing of Ground-Truth Masks in Remote Sensing Semantic Segmentation

Semantic segmentation models are trained and evaluated against human-drawn masks, yet remote-sensing annotations are often coarse, incomplete, or misaligned; high overlap scores may then reflect agreement with imperfect labels rather than faithfulness to the image, creating an evaluation paradox. We introduce Contrastive Mask Fidelity (CMF), a training-free, reference-free metric that scores competing class masks directly against image evidence. CMF composites keep and erase counterfactual views of each mask and asks a frozen vision-language judge whether class evidence is concentrated inside the mask and absent outside. We validate CMF on controlled mask corruptions, then audit 10,731 image-class pairs across ten remote-sensing benchmarks using candidate masks from Seg-Probe, a training-free open-vocabulary probe built on SegEarth-OV3 that outperforms prior baselines on nine of ten datasets. The audit reveals systematic, class-dependent annotation distortion: man-made classes such as buildings, roads, and cars favor the candidate mask on 62-85% of pairs, whereas ambiguous land cover more often favors human annotations. On a blinded three-annotator consensus, CMF matches expert judgment on 81% of pairs, exceeding keep-only scoring, model confidence, and a trained label-quality baseline. Finally, conservative class-wise arbitration yields supervision that improves cross-domain transfer over raw annotations and matched replacement controls, positioning CMF as a scalable tool for auditing ground truth rather than presuming it infallible.

cs.CV↗
arXiv2026

Inference on Generalized Latent Variable Models with High-Dimensional Responses and Covariates

Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response variables depend not only on observed covariates but also on latent variables that capture key unobserved characteristics. To draw statistical inferences on covariate effects while accounting for latent variables, we consider a high-dimensional generalized latent variable model that accommodates mixed-type responses and allows for flexible dependence between covariates and latent variables, which is more suitable for many real-world applications than existing methods that either rely on a linear regression form or restricted assumptions on the dependence between covariates and latent variables. We develop an alternating algorithm that iteratively updates the regression parameters and the latent variables, transforming an intractable nonconvex problem into a sequence of tractable convex subproblems. Theoretically, we provide algorithmic guarantees by establishing statistical consistency of the resulting estimator and deriving an error bound for it. Further, building on this estimator, we construct a debiased estimator for the covariate effect and establish its asymptotic normality. The effectiveness of the proposed method is demonstrated through an application to evaluating the fairness of the Programme for International Student Assessment (PISA).

stat.ME↗
arXiv2026

Beyond Vintage Rotation: Bias-Free Sparse Representation Learning with Oracle Inference

Learning low-dimensional latent representations is a central topic in statistics and machine learning, and rotation methods have long been used to obtain sparse and interpretable representations. Despite nearly a century of widespread use across many fields, rigorous guarantees for valid inference for the learned representation remain lacking. In this paper, we identify a surprisingly prevalent phenomenon that suggests a reason for this gap: for a broad class of vintage rotations, the resulting estimators exhibit a non-estimable bias. Because this bias is independent of the data, it fundamentally precludes the development of valid inferential procedures, including the construction of confidence intervals and hypothesis testing. To address this challenge, we propose a novel bias-free rotation method within a general representation learning framework based on latent variables. We establish an oracle inference property for the learned sparse representations: the estimators achieve the same asymptotic variance as in the ideal setting where the latent variables are observed. To bridge the gap between theory and computation, we develop an efficient computational framework and prove that its output estimators retain the same oracle property. Our results provide a rigorous inference procedure for the rotated estimators, yielding statistically valid and interpretable representation learning. Code for implementing the proposed method is available at the GitHub repository: https://github.com/chengyu06/Folomin.git.

stat.ME↗
arXiv2025

Accounting for Measurement Bias: A New Framework for Reliable Country Ranking in Large-Scale Educational Assessments

International Large-scale Assessments (ILSAs), such as the Program for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS), are cornerstone tools for global educational research and policy-making. By benchmarking educational quality and performance trends, these assessments enable countries to evaluate and share effective pedagogical structures. Specifically, ILSAs employ Item Response Theory (IRT) models to rank countries by students' performance on cognitive items. However, measurement bias--arising from linguistic, cultural, and curricular differences--poses a significant threat to the statistical inference of IRT models and, consequently, the validity of the resulting rankings. Neglecting this bias can lead to systematic errors in parameter estimation, ultimately distorting national standings. To address this, we propose a novel method that avoids the restrictive assumptions typical of existing approaches, such as the prior identification of unbiased anchor items or designated reference groups. Our approach is computationally efficient and provides theoretical guarantees for the reliable recovery of group rankings. We apply this method to PISA 2022 data across the mathematics, science, and reading domains, yielding corrected performance rankings and insights into the survey's measurement-bias structures.

stat.ME↗
arXiv2025

Hilbert expansion of the Boltzmann equation on a 2-dimensional disk with specular boundary condition

In the present paper, we concern the hydrodynamic limit of Boltzmann equation with specular reflection boundary condition in a two-dimensional disk to the compressible Euler equations. Due to the non-zero curvature and non-zero tangential velocity of compressible Euler solution on the boundary, new difficulties arise in the construction of Knudsen boundary layer. By employing the geometric correction, and an innovative and refined $L^2-L^\infty$ method, we establish the existence and space-decay for a truncated Knudsen boundary layer. Then, by the Hilbert expansion of multi-scales, we successfully justify the hydrodynamic limit of Boltzmann equation with specular reflection boundary condition to the compressible Euler equations in the two-dimensional disk.

math.AP↗
arXiv2024

Statistical Inference for Covariate-Adjusted and Interpretable Generalized Factor Model with Application to Testing Fairness

Latent variable models are popularly used to measure latent factors (e.g., abilities and personalities) from large-scale assessment data. Beyond understanding these latent factors, the covariate effect on responses controlling for latent factors is also of great scientific interest and has wide applications, such as evaluating the fairness of educational testing, where the covariate effect reflects whether a test question is biased toward certain individual characteristics (e.g., gender and race), taking into account their latent abilities. However, the large sample sizes and test lengths pose challenges to developing efficient methods and drawing valid inferences. Moreover, to accommodate the commonly encountered discrete responses, nonlinear latent factor models are often assumed, adding further complexity. To address these challenges, we consider a covariate-adjusted generalized factor model and develop novel and interpretable conditions to address the identifiability issue. Based on the identifiability conditions, we propose a joint maximum likelihood estimation method and establish estimation consistency and asymptotic normality results for the covariate effects. Furthermore, we derive estimation and inference results for latent factors and the factor loadings. We illustrate the finite sample performance of the proposed method through extensive numerical studies and an educational assessment dataset from the Programme for International Student Assessment (PISA).

stat.ME↗
arXiv2024

The global well-posedness and Newtonian limit for the relativistic Boltzmann equation in a periodic box

In this paper, we study the Newtonian limit for relativistic Boltzmann equation in a periodic box $\mathbb{T}^3$. We first establish the global-in-time mild solutions of relativistic Boltzmann equation with uniform-in-$\mathfrak{c}$ estimates and time decay rate. Then we rigorously justify the global-in-time Newtonian limits from the relativistic Boltzmann solutions to the solution of Newtonian Boltzmann equation in $L^1_pL^{\infty}_x$. Moreover, if the initial data of Newtonian Boltzmann equation belong to $W^{1,\infty}(\mathbb{T}^3\times\mathbb{R}^3)$, based on a decomposition and $L^2-L^\infty$ argument, the global-in-time Newtonian limit is proved in $L^{\infty}_{x,p}$. The convergence rates of Newtonian limit are obtained both in $L^1_pL^{\infty}_x$ and $L^{\infty}_{x,p}$.

math.AP↗
arXiv2023

A Note on Improving Variational Estimation for Multidimensional Item Response Theory

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of estimating MIRT models prohibits its wide use because many of the extant methods can hardly provide results in a realistic time frame when the number of dimensions, sample size, and test length are large. Instead, variational estimation methods, such as Gaussian Variational Expectation Maximization (GVEM) algorithm, have been recently proposed to solve the estimation challenge by providing a fast and accurate solution. However, results have shown that variational estimation methods may produce some bias on discrimination parameters during confirmatory model estimation, and this note proposes an importance weighted version of GVEM (i.e., IW-GVEM) to correct for such bias under MIRT models. We also use the adaptive moment estimation method to update the learning rate for gradient descent automatically. Our simulations show that IW-GVEM can effectively correct bias with modest increase of computation time, compared with GVEM. The proposed method may also shed light on improving the variational estimation for other psychometrics models.

stat.ME↗
arXiv2023

Hilbert Expansion of Boltzmann Equation with Soft Potentials and Specular Boundary Condition in Half-space

Boundary effects play an important role in the study of hydrodynamic limits in the Boltzmann theory. We justify rigorously the validity of the hydrodynamic limit from the Boltzmann equation of soft potentials to the compressible Euler equations by the Hilbert expansion with multi-scales. Specifically, the Boltzmann solutions are expanded into three parts: interior part, viscous boundary layer and Knudsen boundary layer. Due to the weak effect of collision frequency of soft potentials, new difficulty arises when tackling the existence of Knudsen layer solutions with space decay rate, which has been overcome under some constraint conditions and losing velocity weight arguments.

math.AP↗
arXiv2023

High-Dimensional Inference for Generalized Linear Models with Hidden Confounding

Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, neuroscience, to economics. However, in practice, there are often potential unmeasured confounders associated with both the response and covariates, which can lead to invalidity of standard debiasing methods. This paper focuses on a generalized linear regression framework with hidden confounding and proposes a debiasing approach to address this high-dimensional problem, by adjusting for the effects induced by the unmeasured confounders. We establish consistency and asymptotic normality for the proposed debiased estimator. The finite sample performance of the proposed method is demonstrated through extensive numerical studies and an application to a genetic data set.

stat.ME↗
arXiv2023

Statistical Inference for Noisy Incomplete Binary Matrix

We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focuses on point estimation and prediction. This paper moves one step further toward the statistical inference for binary matrix completion. Under a popular nonlinear factor analysis model, we obtain a point estimator and derive its asymptotic normality. Moreover, our analysis adopts a flexible missing-entry design that does not require a random sampling scheme as required by most of the existing asymptotic results for matrix completion. Under reasonable conditions, the proposed estimator is statistically efficient and optimal in the sense that the Cramer-Rao lower bound is achieved asymptotically for the model parameters. Two applications are considered, including (1) linking two forms of an educational test and (2) linking the roll call voting records from multiple years in the United States Senate. The first application enables the comparison between examinees who took different test forms, and the second application allows us to compare the liberal-conservativeness of senators who did not serve in the Senate at the same time.

math.ST↗
arXiv2023

DIF Statistical Inference without Knowing Anchoring Items

Establishing the invariance property of an instrument is a key step for establishing its measurement validity. Measurement invariance is typically assessed by differential item functioning (DIF) analysis, i.e., detecting DIF items whose response distribution depends on not only the latent trait measured by the instrument but also the group membership. DIF analysis is confounded by the group difference in the latent trait distributions. Many DIF analyses require knowing several anchor items that are DIF-free to draw inferences on whether each of the rest is a DIF item, where the anchor items are used to identify the latent trait distributions. When no prior information on anchor items is available, item purification methods and regularized estimation methods can be used. The former iteratively purifies the anchor set by a stepwise model selection procedure, and the latter selects the DIF-free items by a LASSO-type regularization approach. Unfortunately, unlike the methods based on a correctly specified anchor set, these methods are not guaranteed to provide valid statistical inference (e.g., confidence intervals and $p$-values). In this paper, we propose a new method for DIF analysis under a multiple indicators and multiple causes (MIMIC) model for DIF. This method adopts a minimal $L_1$ norm condition for identifying the latent trait distributions. Without requiring prior knowledge about an anchor set, it can accurately estimate the DIF effects of individual items and further draw valid statistical inferences for quantifying the uncertainty. Specifically, the inference results allow us to control the type-I error for DIF detection, which may not be possible with item purification and regularized estimation methods. The proposed method is applied to analyzing the three personality scales of the Eysenck personality questionnaire - revised (EPQ-R).

stat.ME↗
arXiv2022

Identifiability of Latent Class Models with Covariates

Latent class models with covariates are widely used for psychological, social, and educational research. Yet the fundamental identifiability issue of these models has not been fully addressed. Among the previous research on the identifiability of latent class models with covariates, Huang and Bandeen-Roche (2004, Psychometrika, 69:5-32) studied the local identifiability conditions. However, motivated by recent advances in the identifiability of the restricted latent class models, particularly Cognitive Diagnosis Models (CDMs), we show in this work that the conditions in Huang and Bandeen-Roche (2004) are only necessary but not sufficient to determine the local identifiability of the model parameters. To address the open identifiability issue for latent class models with covariates, this work establishes conditions to ensure the global identifiability of the model parameters in both strict and generic senses. Moreover, our results extend to the polytomous-response CDMs with covariates, which generalizes the existing identifiability results for CDMs.

stat.ME↗
arXiv2011

New Constraints from PAMELA anti-proton data on Annihilating and Decaying Dark Matter

Recently the PAMELA experiment has released its updated anti-proton flux and anti-proton to proton flux ratio data up to energies of ~200GeV. With no clear excess of cosmic ray anti-protons at high energies, one can extend constraints on the production of anti-protons from dark matter. In this letter, we consider both the cases of dark matter annihilating and decaying into standard model particles that produce significant numbers of anti-protons. We provide two sets of constraints on the annihilation cross-sections/decay lifetimes. In the one set of constraints we ignore any source of anti-protons other than dark matter, which give the highest allowed cross-sections/inverse lifetimes. In the other set we include also anti-protons produced in collisions of cosmic rays with interstellar medium nuclei, getting tighter but more realistic constraints on the annihilation cross-sections/decay lifetimes.

astro-ph.HE↗
arXiv2010

Spectral tuning of multimode three-dimensional photonic crystal cavities for enhanced anti-Stokes Raman scattering

Multimode hollow microcavities in three-dimensional (3D) photonic crystals (PhCs) are designed for achieving enhanced coherent anti-Stokes Raman scattering, which requires a cavity to have three high quality-factor (Q) modes with equally spaced resonant frequencies. Cavities in 3D PhCs allows more flexibility in design and tuning than their 2D slab counterparts, since radiation loss that degrades Q can be suppressed by the 3D photonic band gap. We first tune all the mode frequencies simultaneously by changing the material and geometry of the cavity based on perturbation theory. Spectral spacings between the multiple modes are adjusted according to the symmetry, volume and field distribution of their mode profiles. The frequency and field distribution of the resonant modes are computed by solving Maxwell's equations in the frequency domain.

physics.optics↗