Shirley Mathur | Searcharxiv

arXiv2026

Sparse Directed Network Recovery From Multivariate Count Time Series

Count time series arise naturally in many scientific applications. Examples include species abundances collected over time, and gene expression counts over pseudotime. Network inference from such data can yield important insight into underlying biological mechanisms, but is challenging because count observations are discrete, overdispersed, and temporally dependent. We propose a multivariate Poisson log-normal model in which observed counts arise from a Poisson distribution with rates determined by a latent vector autoregressive (VAR) process. The latent VAR transition matrix induces directed temporal dependencies among components and serves as the target network parameter. We establish identifiability of the model under suitable conditions and develop two procedures for sparse estimation of the VAR transition matrix. Our first estimator is a sparse method-of-moments estimator, for which we establish consistent network recovery and parameter estimation in high-dimensional settings. Our second estimator is an L1-penalized variational inference estimator that is motivated by recent approaches for count data. We evaluate the proposed estimators through simulations under a range of sampling regimes. We further demonstrate the method on a macroinvertebrate abundance time series collected from European freshwater ecosystems, obtaining an interpretable ecological network.

stat.ME↗

arXiv2024

Fully Synthetic Data for Complex Surveys

When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993). Specifically, we generate pseudo-populations by applying the weighted finite population Bayesian bootstrap to account for survey weights, take simple random samples from those pseudo-populations, estimate synthesis models using these simple random samples, and release simulated data drawn from the models as public use files. To facilitate variance estimation, we use the framework of multiple imputation with two data generation strategies. In the first, we generate multiple data sets from each simple random sample. In the second, we generate a single synthetic data set from each simple random sample. We present multiple imputation combining rules for each setting. We illustrate the repeated sampling properties of the combining rules via simulation studies, including comparisons with synthetic data generation based on pseudo-likelihood methods. We apply the proposed methods to a subset of data from the American Community Survey.

stat.ME↗