ABSTRACT This paper addresses the fundamental challenge of jointly performing node clustering and representation learning in directed and valued graphs, which needs both global and local network structures to be captured. While these two tasks are highly interdependent, they are often treated separately in existing works. We propose the deep zero‐inflated latent position block model (Deep‐ZLPBM) in the context of directed and valued networks characterized by non‐symmetric adjacency matrices with positive integer entries. Our approach leverages a variational autoencoder (VAE) framework, combining a deep neural network encoder designed to handle directed edges and a zero‐inflated Poisson (ZIP) block modeling decoder to model sparse, integer‐weighted interactions. Recognizing the limitations of the standard evidence lower bound (ELBO) in VAEs, we explore the importance weighted ELBO (iw‐ELBO), a tighter bound on the marginal log‐likelihood optimized via gradient ascent, to enhance inference. Extensive experiments on synthetic datasets demonstrate that iw‐ELBO optimization yields significant performance gains. Moreover, our results validate that Deep‐ZLPBM effectively models complex network structures, providing interpretable partial memberships and insightful visualizations for directed, valued graphs.
ABSTRACT We study covariance estimation in high‐dimensional approximate factor models with a sparse residual covariance. The estimator combines a log‐det loss with nuclear‐norm and off‐diagonal penalties on a sample‐normalized covariance scale. Under light‐tailed sampling, pervasive factors and factor‐space incoherence, we establish local curvature and characterize a local fixed point of the associated threshold iteration. Its low‐rank component consistently recovers the latent rank; exact signed residual‐support recovery additionally requires a minimum‐signal condition. We derive componentwise estimation rates and sufficient conditions for positive definiteness. The analysis separates the spectral requirements for rank recovery from the entrywise requirements for residual‐support recovery. A boundary‐adaptive search identifies the two thresholds by extending an initial grid when its selected solution lies on a boundary. Simulations examine structural recovery and covariance accuracy, and compare log‐det and Frobenius losses with covariance shrinkage methods. An application to European bank supervisory indicators illustrates the resulting decomposition into latent factors and sparse residual relationships.
ABSTRACT Combination treatments (COM) are increasingly used in oncology to boost efficacy, limit resistance, and reduce side effects, requiring new dose‐finding designs that balance safety and efficacy while accounting for potential drug interactions. To meet this need, we propose EWOUC‐NETS‐COM, a novel Bayesian design extending our Escalation with Overdose and Underdose Control (EWOUC) f ramework. This design jointly models toxicity and efficacy through a bivariate normal structure. It incorporates the Normalized Equivalent Toxicity Score (NETS) as a quasi‐continuous toxicity endpoint, enhancing granularity in toxicity assessment of combination treatment. EWOUC‐NETS‐COM introduces three key innovations: (1) a utility‐based allocation algorithm that adapts to emerging drug synergy; (2) two‐dimensional overdose and underdose control for more informed dose exclusion and early stopping; and (3) continuous dual endpoints to reduce information loss and improve precision. Extensive simulations and application in a real‐world trial demonstrate that EWOUC‐NETS‐COM outperforms existing designs in selecting optimal doses, preserving patient safety, ensuring therapeutic efficacy, and maximizing trial efficiency. This practical and flexible design is well‐suited for guiding dose escalation and de‐escalation in two‐drug combination trials and is readily extendable to more complex multi‐agent regimens.
ABSTRACT We study the causal effects of urban amenities on nightly prices in short‐term rental platforms such as Airbnb. Prices are determined both by property characteristics and by their spatial location, which introduces complex local and neighborhood‐level variations. We analyze Airbnb listings in Broward County, Florida, using a two‐stage semiparametric framework with explicit spatial tuning. A generalized additive model captures nonlinear effects of property attributes and broad geographic trends, while a low‐rank Moran eigenvector basis filters residual fine‐scale spatial dependence. This approach provides a data‐driven framework to assess the spatially heterogeneous influence of amenities and neighborhood features, and offers interpretable insights into pricing strategies in densely distributed urban rental markets.
ABSTRACT This paper proposes a novel transfer learning framework for high‐dimensional quantile regression, addressing the non‐differentiability of quantile loss via a Huber approximation and leveraging elastic net regularization to handle sparse inference. By replacing the piecewise linear quantile check function with a smooth Huber loss, our method achieves computational efficiency while preserving robustness to heavy‐tailed errors and outliers. We develop Oracle Trans‐HAQ, a two‐step transfer algorithm that integrates source knowledge through elastic net penalties, and THAQ, a data‐driven detection framework using cross‐validation to mitigate negative transfer risks in scenarios with unknown informative sources. Numerical simulations demonstrate superior performance in high‐dimensional settings, with significantly lower estimation errors compared to convolution‐smoothed quantile regression and pure quantile loss methods. Applied to GTEx genomic data, our method improves prediction accuracy for JAM2 gene expression quantiles across brain tissues, highlighting its utility in precision medicine for modeling heterogeneous biological effects.
