Modern statistical analysis often encounters high-dimensional problems but with a limited sample size. It poses great challenges to traditional statistical estimation methods. In this work, we adopt auxiliary learning to solve the estimation problem in high-dimensional settings. We start with the linear regression setup. To improve the statistical efficiency of the parameter estimator for the primary task, we consider several auxiliary tasks, which share the same covariates with the primary task. Then a weighted estimator for the primary task is developed, which is a linear combination of the ordinary least squares estimators of both the primary task and auxiliary tasks. The optimal weight is analytically derived and the statistical properties of the corresponding weighted estimator are studied. We then extend the weighted estimator to generalized linear regression models. Extensive numerical experiments are conducted to verify our theoretical results. Last, a deep learning-related real-data example of smart vending machines is presented for illustration purposes.

Brand competitive analysis is crucial for businesses to understand their market position and develop strategies to outperform competitors. As consumer behavior and market trends evolve, companies increasingly rely on data-driven insights to stay ahead. In this context, online reviews on E-commerce platforms offer a wealth of information, providing valuable opportunities for analyzing consumer preferences. With the rise of multimodal reviews (combining both text and images), it has become essential to harness these diverse data sources effectively. To address this need, we first propose a multimodal and multi-corpora latent Dirichlet allocation (MM-LDA) model for static data. It uncovers both general topics and brand-specific topics, enabling fine-grained insights into consumer concerns and brand positioning. We further extend MM-LDA into a dynamic framework and propose the multimodal and multi-corpora dynamic topic model (MM-DTM), which incorporates temporal dynamics to capture topic evolution over time. The effectiveness of our approaches are demonstrated using two datasets from ZOL and Baidu Tieba discussions.

We revisit the problem of generating synthetic data under differential privacy. To address the core limitations of marginal-based methods, we propose the Private Adaptive Generative Adversarial Network with Bayes Network Structure (PrAda-GAN), which integrates the strengths of both GAN-based and marginal-based approaches. Our method adopts a sequential generator architecture to capture complex dependencies among variables, while adaptively regularizing the learned structure to promote sparsity in the underlying Bayes network. Theoretically, we establish diminishing bounds on the parameter distance, variable selection error, and Wasserstein distance. Our analysis shows that leveraging dependency sparsity leads to significant improvements in convergence rates. Empirically, experiments on both synthetic and real-world datasets demonstrate that PrAda-GAN outperforms existing tabular data synthesis methods in terms of the privacy–utility trade-off.

Survival analysis problems are crucial in business area. Most of the existing research conducts business survival problem with structured data, yet overlooks the potential rich information provided in text data. Therefore, we creatively extract useful information from online reviews and study the influence of textual features on the occurrence of a business event. We propose a novel dynamic survival topic model (DSTM) that first extract topic proportions from text corpus as topic features in each time slice, then estimate the coefficients each feature contributes to the hazard ratio under time-dependent circumstances. Experiments on two real-word business datasets show that our proposed model can not only identify the true event time in the corpus and how the features influence the event, but also outperform two baseline models in six evaluation metrics. Our findings provide significant implications for decision makers to understand users’ personal attitude towards corresponding event behind their corpus and personal features.

Exploring the emerging knowledge trends within a particular discipline is of great interest to scientific researchers. It helps researchers to understand the historical development of their disciplines and to guide their future research directions. In this work, we focus on the fast-developing discipline, statistics, to investigate its knowledge trend and emerging topics in statistical research. To this end, we collect publications in top-tier statistical journals and statistical-related conferences, and develop a covariate-assisted dynamic topic model (CDTM). It captures the dynamic evolution of topics in statistical publications and also finds the time-varying effects of covariates on topic discussions. To estimate CDTM, a variational inference procedure is applied. The theoretical properties are studied and finite-sample performance is evaluated through simulation experiments. Last, we apply CDTM to the collected academic data. We highlight the advantages of CDTM over other alternative methods and uncover topics that characterize the evolution of statistical knowledge over the past four decades. We also observe that certain research topics in statistics are increasingly aligning with advancements in artificial intelligence. Based on these findings, we gain valuable insights into the historical progression of statistical research, which enables us to better anticipate future trends and guide innovation in the field. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Target-oriented vision-and-language navigation (VLN) on aerial platforms is attracting growing attention for missions such as disaster rescue, infrastructure inspection, and security patrol. In this task, an unmanned aerial vehicle (UAV) needs to locate targets given only a concise description of their appearance and surroundings. This requires global exploration and grounding as well as collision-free close-range approach, two interleaved processes difficult to reconcile within a single agent. Most existing methods transfer the ground VLN paradigm to a low-altitude UAV and compensate for its inefficient exploration with external assistance. A recent attempt deploys two UAVs at complementary altitudes yet still relies on privileged information and trains its two agents independently, precluding any mutual adaptation essential for cooperation. Here we propose CoNav-UAV, which explicitly models the task as a Stackelberg game between a high-altitude leader and a low-altitude follower, with the system operating on onboard visual and linguistic inputs alone. To solve this game, we introduce Iterative Stackelberg Learning. The leader's high-level vision-language reasoning is refined via memory-based in-context learning, while the follower's precise motion control is updated via DAgger-style expert distillation. The alternation drives both agents toward a Stackelberg equilibrium. CoNav-UAV consistently outperforms single- and dual-agent baselines across three high-fidelity urban scenes from the AerialVLN benchmark. Success rate improves by up to 30.8 points on the learning scene, and 9.0 points under cross-scene transfer while using about 3x less adaptation data. Further analyses validate the complementary gains of the leader and follower updates and reveal robust gains yet distinct learning dynamics across VLM backbones.

In recent decades, there is an increasing interest in analyzing two-mode networks, which are constructed by two types of nodes. In two-mode networks, the core nodes (such as the superstars in social platforms) are often encountered in practice. These core nodes could generate different network influences compared to ordinary nodes. Then considering the effect of core nodes in network analysis becomes an important problem. To address this problem, we propose a novel network autoregressive model for two-mode networks with core nodes. It assumes each type of nodes can be split into the core group and the ordinary group. Then a network autoregressive model with different cross-mode effects for the core group and the ordinary group is established. We further extend this model to incorporate the interaction effects between different groups of nodes (e.g., the core group of Type I and the ordinary group of Type II). The theoretical properties of the proposed models are investigated. The finite sample performance is assessed through a variety of simulations. Finally, we apply the proposed models to a real security dataset to explore the influences between fund performance and analysts.

Individuals who are connected to each other in a network are more likely to exhibit similar behaviours. This implies that the formation of a dyadic link could be inferred from common factors shared by two nodes. In this study, we propose a varying coefficient dyadic interaction model with a random effect to understand user relationships in social networks. Specifically, we assume that the generation of a dyadic link is driven by an observed common factor. Owing to the existence of heterogeneity, we assume that the influence of the common factor is a linear combination of several moderate variables. Obtaining a maximum likelihood estimator in this situation is infeasible. To address this issue, we propose a pseudo-maximum likelihood estimator, which is feasible and can substantially alleviate the computational cost. Although the proposed estimator is not as accurate as a maximum likelihood estimator, it can be used when heterogeneity exists or the sample size is extremely large. We then conduct numerical studies to assess the finite sample performance of the proposed method. Finally, to empirically examine the usefulness and practicality of the proposed method, we apply the model to two different real-world datasets. The empirical results demonstrate the influence of common factors on dyadic link generation.

Bipartite networks containing two types of nodes are commonly encountered in practice. To analyze bipartite networks, network embedding models are popularly used. With the increase in data availability, nodes in networks are often observed with nodal attributes, which provide fertile information for understanding the network structure. However, existing network embedding models for dynamic bipartite networks often ignore nodal variables. To address this problem, we propose a latent space model for bipartite networks by incorporating information from both the covariates and the network structure. To reflect the evolution pattern of the network structure, we introduce two parameters representing the persistence effect. To estimate the model, we propose a computationally efficient algorithm using the projected gradient descent method. The theoretical properties are also established and validated through comprehensive simulation studies. Last, we analyze the dynamic bipartite network for the Chinese interlocking directorates from 2010 to 2020 using our proposed model.

Over the past twenty years, topic modeling has gradually become popular as a powerful tool, extracting useful and meaningful latent representations from large texts. Research on topic evolution, focusing on the representation of changes in topics over time, has begun to attract extensive attention in the fields of information retrieval and data mining. The dynamic topic model is a classical model for topic evolution. It assumes all topics exist throughout the entire time period, overlooking the fact that topics that were previously important are no longer considered, and new topics can also emerge. To address this issue, we propose a novel Bayesian sparse dynamic topic model, utilizing a spike-and-slab prior distribution to capture topic birth and death. The results demonstrate that our proposed model can effectively estimate both the topic distribution and topic sparsity at the same time. Furthermore, simulations and empirical studies on two real-world datasets demonstrate that our proposed model outperforms the classical dynamic topic model and provides rich semantic information on focused topics.

Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. By transfer learning sufficiency (denoted as the null hypothesis), we mean that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.

The prevalence of multimodal data has become commonplace in e-commerce platforms. Both seller showcases (i.e., the seller’s show) and user-generated content (i.e., the buyer’s show) now incorporate diverse modalities, combining both textual and visual elements. In this work, we aim to unraveling the impact of seller’s show on buyer’s show through the anchoring effect. We narrow our research on the specific problem of review helpfulness prediction and further explore whether the anchoring effect can improve the prediction accuracy of review helpfulness. In pursuit of this goal, we develop the Multi-granularity Attention Network Model based on Anchoring Effect (MAN-AE). This model first extracts the multi-granularity features in both seller’s show and buyer’s show and then accounts for the anchoring effect through a cross-source transformer. Through extensive experiments on an Amazon dataset, we demonstrate the anchoring effect of seller’s show on buyer’s show in enhancing the review helpfulness prediction performance. In comparison with other state-of-the-art models, our model demonstrates significantly superior prediction performance.

Detecting knowledge emerging trends has received increasing attention. It can help researchers understand the history of the discipline and predict future research hotspots. Dynamic topic models can be used to identify knowledge emerging trends from academic papers. However, traditional dynamic topic models have some shortcomings, such as over-assumptions, insufficient topic distinction, and high computational cost. To address this problem, we propose a relevance-based dynamic thin topic model (RBDTTM). We model topic evolution with a Gaussian process and adopt a relevance-based mechanism on topic-word distributions. Under this assumption, only words relevant to a certain topic can be represented. This relevance-based mechanism can not only decrease the number of parameters to be estimated but also achieve more prominent and focused topics. We evaluate the estimation performance of RBDTTM using a series of experiments on synthetic data. Results show that RBDTTM has greater interpretability and generalization than its competitors. Finally, we take the statistics discipline as an example and apply RBDTTM to two corpora of journal articles and a Chinese graduation thesis to explore the emerging statistical knowledge trend in the past two decades.

In the digital age, social media platforms have seen a surge in user-generated content, particularly short-form we-media content. Traditional topic modeling methods often struggle to effectively analyze such content due to their limited generalization ability and interpretability. To address this issue, we propose the Co-occurrence Graph Topic Model (COGTM), a novel approach designed to enhance topic modeling in the context of long-short text co-occurrence scenarios. COGTM leverages the inherent interconnectedness between short and associated long-texts, as well as semantically similar words, within the text corpus. By incorporating these associations into the modeling process, COGTM aims to capture richer semantic information and improve the interpretability of the learned topics. Empirical analysis demonstrates that COGTM outperforms baseline models in various text classification and clustering tasks. By effectively capturing the latent associations between different types of text elements, COGTM offers a promising approach to topic modeling in scenarios involving diverse and interconnected textual data.

In decentralized federated learning (DFL), the presence of abnormal clients, often caused by noisy or poisoned data, can significantly disrupt the learning process and degrade the overall robustness of the model. Previous methods on this issue often require a sufficiently large number of normal neighboring clients or prior knowledge of reliable clients, which reduces the practical applicability of DFL. To address these limitations, we develop here a novel adaptive DFL (aDFL) approach for robust estimation. The key idea is to adaptively adjust the learning rates of clients. By assigning smaller rates to suspicious clients and larger rates to normal clients, aDFL mitigates the negative impact of abnormal clients on the global model in a fully adaptive way. Our theory does not put any stringent conditions on neighboring nodes and requires no prior knowledge. A rigorous convergence analysis is provided to guarantee the oracle property of aDFL. Extensive numerical experiments demonstrate the superior performance of the aDFL method.

Automated international classification of diseases (ICD) coding aims to assign multiple disease codes to clinical documents and plays a critical role in healthcare informatics. However, its performance is hindered by the extreme long-tail distribution of the ICD ontology, where a few common codes dominate while thousands of rare codes have very few examples. To address this issue, we propose a Probability-Biased Directed Graph Attention model (ProBias) that partitions codes into common and rare sets and allows information to flow only from common to rare codes. Edge weights are determined by conditional co-occurrence probabilities, which guide the attention mechanism to enrich rare-code representations with clinically related signals. To provide higher-quality semantic representations as model inputs, we further employ large language models to generate enriched textual descriptions for ICD codes, offering external clinical context that complements statistical co-occurrence signals. Applied to automated ICD coding, our approach significantly improves the representation and prediction of rare codes, achieving state-of-the-art performance on three benchmark datasets. In particular, we observe substantial gains in macro-averaged F1 score, a key metric for long-tail classification.

Identifying change points in dynamic text data is crucial for understanding the evolving nature of topics across various sources, such as news articles, scientific papers, and social media posts. While topic modeling has become a widely used technique for this purpose, capturing fine-grained shifts in individual topics over time remains a significant challenge. Traditional approaches typically use a two-stage process, separating topic modeling and change point detection. However, this separation can lead to information loss and inconsistency in capturing subtle changes in topic evolution. To address this issue, we propose TOPIC-PYP, a change point detection model specifically designed for fine-grained topic-level analysis, i.e., detecting change points for each individual topic. By leveraging the Pitman-Yor process, TOPIC-PYP effectively captures the dynamic evolution of topic meanings over time. Unlike traditional methods, TOPIC-PYP integrates topic modeling and change point detection into a unified framework, facilitating a more comprehensive understanding of the relationship between topic evolution and change points. Experimental evaluations on both synthetic and real-world datasets demonstrate the effectiveness of TOPIC-PYP in accurately detecting change points and generating high-quality topics.

Small and medium-sized enterprises (SMEs) play a crucial role in driving economic growth. Monitoring their financial performance and discovering relevant covariates are essential for risk assessment, business planning, and policy formulation. This paper focuses on predicting profits for SMEs. Two major challenges are faced in this study: 1) SMEs data are stored across different institutions, and centralized analysis is restricted due to data security concerns; 2) data from various institutions contain different levels of missing values, resulting in a complex missingness issue. To tackle these issues, we introduce an innovative approach named Vertical Federated Expectation Maximization (VFEM), designed for federated learning under a missing data scenario. We embed a new EM algorithm into VFEM to address complex missing patterns when full dataset access is unfeasible. Furthermore, we establish the linear convergence rate for the VFEM and establish a statistical inference framework, enabling covariates to influence assessment and enhancing model interpretability. Extensive simulation studies are conducted to validate its finite sample performance. Finally, we thoroughly investigate a real-life profit prediction problem for SMEs using VFEM. Our findings demonstrate that VFEM provides a promising solution for addressing data isolation and missing values, ultimately improving the understanding of SMEs' financial performance.

Federated learning is a novel framework for distributed learning, which aims to break isolated data islands, as well as protect data privacy. To further prevent privacy leakage by specially crafted attacks, differential privacy is often integrated. Although differential privacy effectively secures sensitive information, it can reduce the statistical efficiency of the resulting estimators. This leads to a trade-off relationship between statistical efficiency and privacy protection. To theoretically understand this relationship, we start with the classic linear regression model and a noise-adding federated gradient descent algorithm. Its numerical convergence properties and asymptotic properties are rigorously studied. This results in fruitful insights into the trade-off relationship between statistical efficiency and privacy protection. Guided by these theoretical understandings, we further develop a Polyak-Ruppert-type averaged estimator, which can achieve good statistical efficiency with guaranteed privacy protection. Extensive simulation studies are presented to corroborate our theoretical results. Finally, we illustrate the application of our proposed method on an enterprise community data set. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: Financial support from the National Natural Science Foundation of China [Grants 12401386, 72371241, 72495123, and 12271012], the Ministry of Education Project of Key Research Institute of Humanities and Social Sciences [Grant 22JJD910001], the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation [Grant GZB20230070], and the Beijing Municipal Social Science Foundation [Grant 24GLC033] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0554 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0554 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .