arXiv subjects

Yen-hsuan Tseng

Publications and source records attributed to Yen-hsuan Tseng.

4 recordsLinked to original sources

arXiv2026

Recovering Unobserved Network Links from Aggregated Relational Data: Bayesian Latent Surface Modeling and Penalized Regression

Aggregated relational data (ARD) record counts of ties to attribute-defined groups while leaving individual edges unobserved. We compare latent-geometry and regularized network estimators through a common observation map. The comparison distinguishes the realized adjacency matrix, conditional edge probabilities, and model parameters. We study roster-based ARD with known node-level group memberships, giving both estimators the same roster and aggregate counts. We relate the aggregate means to a Poisson working likelihood and a Huber loss, and give their derivatives. Overlapping groups, shared edges, and reporting error affect the interpretation of these objectives. Geometry restricts the representation of edge probabilities, while regularization selects among candidate fits. Identification depends on the observation map and model restrictions rather than uniqueness of a numerical optimizer. A reproducible synthetic experiment specifies the data-generating process, estimation algorithms, and evaluation targets under matched information. The matrix estimator gives better realized-edge rankings and aggregate fit, while the geometric estimator gives lower error for generating probabilities. The resulting framework organizes ARD reconstruction around the interaction of observation design, structural assumptions, and computation.

econ.EM↗

arXiv2026

Activity-Conditioned Residual Association from Aggregated Relational Data

Aggregated relational data (ARD) record how many ties sampled respondents have to prespecified groups without revealing individual dyads. We develop a conditional test for prespecified cross-group concordance beyond an additive-activity network model. When the groups form an exhaustive partition, respondent degree is observed exactly. For arbitrary fixed activity values under an independent-Bernoulli additive-logit null, conditioning two respondents on equal degree yields a finite nonpositive sign restriction for their two disjoint group-count differences. Analyst-randomized pair thinning gives a conservative baseline. Under a stronger fixed-cell design with group-specific smooth activity profiles and positive cross-role overlap, an observable clipped full-pair statistic gives conservative one-network inference with graph-independent sampled respondents. Finite and growing matched-law constructions establish that the target contains bivariate information absent from a collapsed binary count. The full-pair procedure is uniformly consistent on a primitive relative-open neighborhood of a specified rank-one alternative. Fixed-dimensional lattice local limits make the conditioning and availability requirements explicit.

stat.ME↗

arXiv2026

Network recovery from aggregated relational data: An impossibility theorem

We prove an impossibility theorem for uniformly consistent recovery of labeled edge probabilities from complete aggregated relational data, even when the population count law identifies every probability. For any known partition into two equal trait groups, we consider an independent-edge logistic network with a balanced rank-one signal and unknown activity effects with bounded total dyadic energy. The signal amplitude is known and fixed at a small positive value. Every node reports its counts to both groups. The minimax mean squared error for the probability matrix remains bounded away from zero as the network grows, whereas it tends to zero under full adjacency on the same parameter class. The lower bound accounts for the dependence across the entire count array through a two-node oracle from which all observed counts can be reconstructed. Conditional binomial smoothing bounds the information about a local sign orientation, and an anchored many-bit construction converts these ambiguities into a nonvanishing normalized matrix loss. A mixed-cumulant identity recovers every edge probability from the population degree law, locating the obstruction in estimation from a single aggregated network rather than in population identification.

math.ST↗

arXiv2025

Inverse Gaussian Distribution, Introduction and Applications:Comprehensive Analysis of Power Plant Performance: A Study of Combined Cycle and Nuclear Power Plant

This paper presents a comprehensive analysis of power plant performance using the inverse Gaussian (IG) distribution framework. We combine theoretical foundations with practical applications, focusing on both combined cycle and nuclear power plant contexts. The study demonstrates the advantages of the IG distribution in modeling right-skewed industrial data, particularly in power generation. Using the UCI Combined Cycle Power Plant Dataset, we establishthe superiority of IG-based models over traditional approaches through rigorous statistical testing and model validation. The methodology developed here extends naturally to nuclear power plant applications, where similar statistical patterns emerge in operational data. Our findings suggest that IG-based models provide more accurate predictions and better capture the underlying physical processes in power generation systems.

stat.AP↗