arXiv subjects

Pengfei Li

Publications and source records attributed to Pengfei Li.

At least 91 records · Page 5Linked to original sources

arXiv2025

A powerful procedure that controls the false discovery rate with directional information

In many multiple testing applications in genetics, the signs of test statistics provide useful directional information, such as whether genes are potentially up- or down-regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are $p$-value based and ignore such directional information. We introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the FDR in finite samples. We demonstrate the power advantage of our procedure through simulation studies and two real applications.

stat.ME↗

arXiv2025

Maximum likelihood abundance estimation from capture-recapture data when covariates are missing at random

In capture-recapture experiments, individual covariates may be subject to missing, especially when the number of times of being captured is small. When the covariate information is missing at random, the inverse probability weighting method and multiple imputation method are widely used to obtain the point estimators of the abundance. These point estimators are then used to construct the Wald-type confidence intervals for the abundance. However, such intervals may have severely inaccurate coverage probabilities and their lower limits can be even less than the number of individuals ever captured. In this paper, we proposed a maximum empirical likelihood estimation approach for the abundance in presence of missing covariates. We show that the maximum empirical likelihood estimator is asymptotically normal, and that the empirical likelihood ratio statistic for abundance has a chisquare limiting distribution with one degree of freedom. Simulations indicate that the proposed estimator has smaller mean square error than the existing estimators, and the proposed empirical likelihood ratio confidence interval usually has more accurate coverage probabilities than the existing Wald-type confidence intervals. We illustrate the proposed method by analyzing the bird species yellow-bellied prinia collected in Hong Kong.

stat.ME↗

arXiv2025

A mixture distribution approach for assessing genetic impact from twin study

This work was motivated by a twin study with the goal of assessing the genetic control of immune traits. We propose a mixture bivariate distribution to model twin data where the underlying order within a pair is unclear. Though estimation from mixture distribution is usually subject to low convergence rate, the combined likelihood, which is constructed over monozygotic and dizygotic twins combined, reaches root-n consistency and allows effective statistical inference on the genetic impact. The method is applicable to general unordered pairs.

stat.ME↗

arXiv2025

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model

Meta analysis is commonly-used to synthesize multiple results from individual studies. However, its validation is usually threatened by publication bias and between-study heterogeneity, which can be captured by the Copas selection model. Existing inference methods under this model are all based on conditional likelihood and may not be fully efficient. In this paper, we propose a full likelihood approach to meta analysis by integrating the conditional likelihood and a marginal semi-parametric empirical likelihood under a Copas-like selection model. We show that the maximum likelihood estimators (MLE) of all the underlying parameters have a jointly normal limiting distribution, and the full likelihood ratio follows an asymptotic central chisquare distribution. Our simulation results indicate that compared with the conditional likelihood method, the proposed MLEs have smaller mean squared errors and the full likelihood ratio confidence intervals have more accurate coverage probabilities. A real data example is analyzed to show the advantages of the full likelihood method over the conditional likelihood method.

stat.ME↗

arXiv2025

On consistency of the MLE under finite mixtures of location-scale distributions with a structural parameter

We provide a general and rigorous proof for the strong consistency of maximum likelihood estimators of the cumulative distribution function of the mixing distribution and structural parameter under finite mixtures of location-scale distributions with a structural parameter. The consistency results do not require the parameter space of location and scale to be compact. We illustrate the results by applying them to finite mixtures of location-scale distributions with the component density function being one of the commonly used density functions: normal, logistic, extreme-value, or $t$. An extension of the strong consistency results to finite mixtures of multivariate elliptical distributions is also discussed.

math.ST↗

arXiv2025

Controlling IER and EER in replicated regular two-level factorial experiments

Replicated regular two-level factorial experiments are very useful for industry. The goal of these experiments is to identify active effects that affect the mean and variance of the response. Hypothesis testing procedures are widely used for this purpose. However, the existing methods give results that are either too anticonservative or conservative in controlling the individual and experimentwise error rates (IER and EER). In this paper, we propose {a Monte Carlo method} and an exact-variance method to identify active effects for the mean and variance, respectively, of the response. Simulation studies show that our methods control the IER and EER extremely well. Real data are used to illustrate the performance of the methods.

stat.ME↗

arXiv2025

Composite empirical likelihood for multisample clustered data

In many applications, data cluster. Failing to take the cluster structure into consideration generally leads to underestimated variances of point estimators and inflated type I errors in hypothesis tests. Many circumstance-dependent approaches have been developed to handle clustered data. A working covariance matrix may be used in generalized estimating equations. One may throw out the cluster structure and use only the cluster means, or explicitly model the cluster structure. Our interest is the case where multiple samples of clustered data are collected, and the population quantiles are particularly important. We develop a composite empirical likelihood for clustered data under a density ratio model. This approach avoids parametric assumptions on the population distributions or the cluster structure. It efficiently utilizes the common features of the multiple populations and the exchangeability of the cluster members. We also develop a cluster-based bootstrap method to provide valid variance estimation and to control the type I errors. We examine the performance of the proposed method through simulation experiments and illustrate its usage via a real-world example.

stat.ME↗

arXiv2025

Hypothesis testing for quantitative trait locus effects in both location and scale in genetic backcross studies

Testing the existence of a quantitative trait locus (QTL) effect is an important task in QTL mapping studies. Most studies concentrate on the case where the phenotype distributions of different QTL groups follow normal distributions with the same unknown variance. In this paper we make a more general assumption that the phenotype distributions come from a location-scale distribution family. We derive the limiting distribution of the LRT for the existence of the QTL effect in both location and scale in genetic backcross studies. We further identify an explicit representation for this limiting distribution. As a complement, we study the limiting distribution of the LRT and its explicit representation for the existence of the QTL effect in the location only. The asymptotic properties of the LRTs under a local alternative are also investigated. Simulation studies are used to evaluate the asymptotic results, and a real-data example is included for illustration.

stat.ME↗

arXiv2025

Semiparametric empirical likelihood inference for abundance from one-inflated capture-recapture data

Abundance estimation from capture-recapture data is of great importance in many disciplines. Analysis of capture-recapture data is often complicated by the existence of one-inflation and heterogeneity problems. Simultaneously taking these issues into account, existing abundance estimation methods are usually constructed on the basis of conditional likelihood (CL) under one-inflated zero-truncated count models. However, the resulting Horvitz-Thompson-type estimators may be unstable, and the resulting Wald-type confidence intervals may exhibit severe undercoverage. In this paper, we propose a semiparametric empirical likelihood (EL) approach to abundance estimation under one-inflated binomial and Poisson regression models. We show that the maximum EL estimator for the abundance follows an asymptotically normal distribution and that the EL ratio statistic of abundance follows a limiting chi-square distribution with one degree of freedom. To facilitate computation of the EL method, we develop an expectation-maximization (EM) algorithm, and establish its appealing convergence property. We also propose a new score test for the existence of one-inflation and prove its asymptotic normality. Our simulation studies indicate that compared with CL-based methods, the maximum EL estimator has a smaller mean square error, the EL ratio confidence interval has a remarkable gain in coverage probability, and the proposed score test is more powerful. The advantages of the proposed approaches are further demonstrated by analyses of prinia data from Hong Kong and drug user data from Bangkok.

stat.ME↗

arXiv2025

Two-step semiparametric empirical likelihood inference from capture-recapture data with missing covariates

Missing covariates are not uncommon in capture-recapture studies. When covariate information is missing at random in capture-recapture data, an empirical full likelihood method has been demonstrated to outperform conditional-likelihood-based methods in abundance estimation. However, the fully observed covariates must be discrete, and the method is not directly applicable to continuous-time capture-recapture data. Based on the Binomial and Poisson regression models, we propose a two-step semiparametric empirical likelihood approach for abundance estimation in the presence of missing covariates, regardless of whether the fully observed covariates are discrete or continuous. We show that the maximum semiparametric empirical likelihood estimators for the underlying parameters and the abundance are asymptotically normal, and more efficient than the counterpart for a completely known non-missingness probability. After scaling, the empirical likelihood ratio test statistic for abundance follows a limiting chi-square distribution with one degree of freedom. The proposed approach is further extended to one-inflated count regression models, and a score-like test is constructed to assess whether one-inflation exists among the number of captures. Our simulation shows that, compared with the previous method, the proposed method not only performs better in correcting bias, but also has a more accurate coverage in the presence of fully observed continuous covariates, although there may be a slight efficiency loss when the fully observed covariates are only discrete. The performance of the new method is illustrated by an analysis of the Hong Kong prinia data.

stat.ME↗

arXiv2025

World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model

End-to-end autonomous driving directly generates planning trajectories from raw sensor data, yet it typically relies on costly perception supervision to extract scene information. A critical research challenge arises: constructing an informative driving world model to enable perception annotation-free, end-to-end planning via self-supervised learning. In this paper, we present World4Drive, an end-to-end autonomous driving framework that employs vision foundation models to build latent world models for generating and evaluating multi-modal planning trajectories. Specifically, World4Drive first extracts scene features, including driving intention and world latent representations enriched with spatial-semantic priors provided by vision foundation models. It then generates multi-modal planning trajectories based on current scene features and driving intentions and predicts multiple intention-driven future states within the latent space. Finally, it introduces a world model selector module to evaluate and select the best trajectory. We achieve perception annotation-free, end-to-end planning through self-supervised alignment between actual future observations and predicted observations reconstructed from the latent space. World4Drive achieves state-of-the-art performance without manual perception annotations on both the open-loop nuScenes and closed-loop NavSim benchmarks, demonstrating an 18.1\% relative reduction in L2 error, 46.7% lower collision rate, and 3.75 faster training convergence. Codes will be accessed at https://github.com/ucaszyp/World4Drive.

cs.CV↗

arXiv2025

The effect of measurement uncertainties on the inferred stability of planes of satellite galaxies

Observations have revealed that the MW, Andromeda, Centaurus A (and potentially other galaxies) host spatially thin and kinematically coherent planes of satellites. Such structures are highly improbable within the standard LCDM cosmological model, and the dynamical stability of these planes has been a subject of debate for a long time. Accurately determining their stability requires a thorough understanding of orbital parameters such as proper motion, distance, and line-of-sight velocity, in addition to the gravitational potential of the host galaxy. However, many of these remain insufficiently constrained, leading to significant uncertainties in any analysis. This research aims to explore the impact of measurement errors in proper motions and distances of the satellite galaxies and in the adopted host halo mass on the inferred stability of satellite planes in Milky-Way-like potentials. Test satellite galaxies orbiting a host galaxy are simulated, mock observed by adding various degrees and types of observational errors, and then backward-integrated. Trends and correlations between the initial conditions and the applied uncertainties on the inferred orbital stability of the satellite systems are analyzed. Additionally, the effects of adopting incorrect potentials and the impact of different orbital eccentricities are considered. Uncertainties in proper motions lead to an inferred, ostensible widening of an intrinsically stable satellite plane, with its width increasing linearly with the adopted proper motion uncertainties. Even uncertainties on the level of Gaia systematics strongly affect the plane's inferred past width. Moreover, the potential with a low halo mass showed a significant impact on the stability of these planes, while the remaining two host models showed similar effects. Uncertainties in satellite distance also contribute noticeably to the inferred, apparent instability.

astro-ph.GA↗

arXiv2025

MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. The M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems including sandbox-based, real-world software engineering environments. In addition to M1's inherent efficiency advantage for RL training, we propose CISPO, a novel RL algorithm to further enhance RL efficiency. CISPO clips importance sampling weights rather than token updates, outperforming other competitive RL variants. Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on 512 H800 GPUs to complete in only three weeks, with a rental cost of just $534,700. We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training. Experiments on standard benchmarks show that our models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks. We publicly release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1.

cs.CL↗

arXiv2025

FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment

Multi-view multi-object tracking (MVMOT) has found widespread applications in intelligent transportation, surveillance systems, and urban management. However, existing studies rarely address genuinely free-viewpoint MVMOT systems, which could significantly enhance the flexibility and scalability of cooperative tracking systems. To bridge this gap, we first construct the Multi-Drone Multi-Object Tracking (MDMOT) dataset, captured by mobile drone swarms across diverse real-world scenarios, initially establishing the first benchmark for multi-object tracking in arbitrary multi-view environment. Building upon this foundation, we propose \textbf{FusionTrack}, an end-to-end framework that reasonably integrates tracking and re-identification to leverage multi-view information for robust trajectory association. Extensive experiments on our MDMOT and other benchmark datasets demonstrate that FusionTrack achieves state-of-the-art performance in both single-view and multi-view tracking.

cs.CV↗

arXiv2025

Pure-quartic domain-wall solitons as topological bits for data transmission

Domain walls (DWs) are topological defects produced by symmetry-breaking phase transitions. Although DWs have been the subject of much work due to their fundamental physical properties, they have not been explored in optical systems with higher-order dispersion. Recent experimental and theoretical works have demonstrated that pure-quartic (PQ) solitons, with their specific energy-width scaling, arise from the interplay of the quartic group-velocity dispersion (GVD) and Kerr nonlinearity. Here, we report solutions for PQ-DW solitons for the model of optical media with the PQ GVD. The analysis demonstrates that they are stable modes. Further investigation reveals their potential as data carriers for optical telecommunications. These results broaden the variety of optical solitons maintained by diverse nonlinear media.

physics.optics↗

arXiv2025

Effective climate policies for major emission reductions of ozone precursors: Global evidence from two decades

Despite policymakers deploying various tools to mitigate emissions of ozone (O\textsubscript{3}) precursors, such as nitrogen oxides (NO\textsubscript{x}), carbon monoxide (CO), and volatile organic compounds (VOCs), the effectiveness of policy combinations remains uncertain. We employ an integrated framework that couples structural break detection with machine learning to pinpoint effective interventions across the building, electricity, industrial, and transport sectors, identifying treatment effects as abrupt changes without prior assumptions about policy treatment assignment and timing. Applied to two decades of global O\textsubscript{3} precursor emissions data, we detect 78, 77, and 78 structural breaks for NO\textsubscript{x}, CO, and VOCs, corresponding to cumulative emission reductions of 0.96-0.97 Gt, 2.84-2.88 Gt, and 0.47-0.48 Gt, respectively. Sector-level analysis shows that electricity sector structural policies cut NO\textsubscript{x} by up to 32.4\%, while in buildings, developed countries combined adoption subsidies with carbon taxes to achieve 42.7\% CO reductions and developing countries used financing plus fuel taxes to secure 52.3\%. VOCs abatement peaked at 38.5\% when fossil-fuel subsidy reforms were paired with financial incentives. Finally, hybrid strategies merging non-price measures (subsidies, bans, mandates) with pricing instruments delivered up to an additional 10\% co-benefit. These findings guide the sequencing and complementarity of context-specific policy portfolios for O\textsubscript{3} precursor mitigation.

stat.AP↗

arXiv2025

Evaluating Menu OCR and Translation: A Benchmark for Aligning Human and Automated Evaluations in Large Vision-Language Models

The rapid advancement of large vision-language models (LVLMs) has significantly propelled applications in document understanding, particularly in optical character recognition (OCR) and multilingual translation. However, current evaluations of LVLMs, like the widely used OCRBench, mainly focus on verifying the correctness of their short-text responses and long-text responses with simple layout, while the evaluation of their ability to understand long texts with complex layout design is highly significant but largely overlooked. In this paper, we propose Menu OCR and Translation Benchmark (MOTBench), a specialized evaluation framework emphasizing the pivotal role of menu translation in cross-cultural communication. MOTBench requires LVLMs to accurately recognize and translate each dish, along with its price and unit items on a menu, providing a comprehensive assessment of their visual understanding and language processing capabilities. Our benchmark is comprised of a collection of Chinese and English menus, characterized by intricate layouts, a variety of fonts, and culturally specific elements across different languages, along with precise human annotations. Experiments show that our automatic evaluation results are highly consistent with professional human evaluation. We evaluate a range of publicly available state-of-the-art LVLMs, and through analyzing their output to identify the strengths and weaknesses in their performance, offering valuable insights to guide future advancements in LVLM development. MOTBench is available at https://github.com/gitwzl/MOTBench.

cs.LG↗

arXiv2025

Skeleton-Guided Diffusion Model for Accurate Foot X-ray Synthesis in Hallux Valgus Diagnosis

Medical image synthesis plays a crucial role in providing anatomically accurate images for diagnosis and treatment. Hallux valgus, which affects approximately 19% of the global population, requires frequent weight-bearing X-rays for assessment, placing additional strain on both patients and healthcare providers. Existing X-ray models often struggle to balance image fidelity, skeletal consistency, and physical constraints, particularly in diffusion-based methods that lack skeletal guidance. We propose the Skeletal-Constrained Conditional Diffusion Model (SCCDM) and introduce KCC, a foot evaluation method utilizing skeletal landmarks. SCCDM incorporates multi-scale feature extraction and attention mechanisms, improving the Structural Similarity Index (SSIM) by 5.72% (0.794) and Peak Signal-to-Noise Ratio (PSNR) by 18.34% (21.40 dB). When combined with KCC, the model achieves an average score of 0.85, demonstrating strong clinical applicability. The code is available at https://github.com/midisec/SCCDM.

eess.IV↗