arXiv subjects

Shyam Ranganathan

Publications and source records attributed to Shyam Ranganathan.

4 recordsLinked to original sources

arXiv2026

Publish or Perish: A population-dynamics model of the corruption of peer review

Peer review is commonly treated as a mechanism of quality control. This article examines what happens when participants are split between two groups. Explicators assess whether submissions present logic-based arguments (whether deductive, inductive or abductive), independently of whether they agree with the reasons or the conclusion. Interpreters assess submissions according to their outlook. Their employment of logic is opportunistic, as a means of presenting their own views, but not as a means of assessing others' views. Using a deterministic population-dynamics model of iterative games of "Publish or Perish," this article shows how this asymmetry permits interpreters to increase their population share even when they begin as a small minority. It compares fixed and survivor-drawn reviewer panels, alternative replenishment rules, hostile explicator responses, generational differences in ratios, and different panel sizes. Under survivor-drawn review, explicator hostility to interpreters produces an unstable equilibrium at an explicator share of $q^* = 2 - \sqrt{3} \approx 0.2679$: below this threshold, interpreters can survive without explicator cooperation and continue toward dominance. The threshold is not universal, however. Exogenous replenishment in favour of explicators can eliminate the point of no return, and sufficiently lagged, generational replenishment can permit explicators to recover after it has apparently been crossed. The model therefore identifies not only a mechanism by which peer review becomes corrupted, but also the institutional conditions determining whether that corruption becomes self-sustaining or reversible.

physics.soc-ph↗

arXiv2022

Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information

In data science, vector autoregression (VAR) models are popular in modeling multivariate time series in the environmental sciences and other applications. However, these models are computationally complex with the number of parameters scaling quadratically with the number of time series. In this work, we propose a so-called neighborhood vector autoregression (NVAR) model to efficiently analyze large-dimensional multivariate time series. We assume that the time series have underlying neighborhood relationships, e.g., spatial or network, among them based on the inherent setting of the problem. When this neighborhood information is available or can be summarized using a distance matrix, we demonstrate that our proposed NVAR method provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real application of stream nitrogen study.

stat.ME↗

arXiv2021

JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction

Analysis of online reviews has attracted great attention with broad applications. Often times, the textual reviews are coupled with the numerical ratings in the data. In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction. The key of the proposed method is to develop a unified generative model where the topic modeling is constructed based on review texts and the sentiment prediction is obtained by combining review texts and overall ratings. The inference of model parameters are obtained by an efficient Gibbs sampling procedure. The proposed method can enhance the prediction accuracy of review data and achieve an effective detection of interpretable topics and sentiments. The merits of the proposed method are elaborated by the case study from Amazon datasets and simulation studies.

cs.CL↗

arXiv2020

Bayesian Auxiliary Variable Model for Birth Records Data with Qualitative and Quantitative Responses

Many applications involve data with qualitative and quantitative responses. When there is an association between the two responses, a joint model will provide improved results than modeling them separately. In this paper, we propose a Bayesian method to jointly model such data. The joint model links the qualitative and quantitative responses and can assess their dependency strength via a latent variable. The posterior distributions of parameters are obtained through an efficient MCMC sampling algorithm. The simulation shows that the proposed method can improve the prediction capacity for both responses. We apply the proposed joint model to the birth records data acquired by the Virginia Department of Health and study the mutual dependence between preterm birth of infants and their birth weights.

stat.ME↗