Javier Espinosa | Searcharxiv

arXiv2025

Insights from a 30-Year international Partnership on Astronomical Archives

In an era where astronomical data is expanding at an unprecedented rate, the importance of data sharing and accessibility among astronomy archives cannot be overstated. Since the 1990s, an international partnership between the Space Telescope Science Institute (STScI), the European Space Astronomy Centre (ESAC), and the Canadian Astronomy Data Centre (CADC) has been focused on this endeavor, facilitating the exchange of data from the Hubble and James Webb Space Telescopes. We will present how this collaboration has evolved over time, highlighting key milestones and innovations in decision-making, communication, and technology. Additionally, we will discuss some of the challenges we have encountered and the strategies we employed to overcome them, offering insights that could benefit future archive collaborations.

astro-ph.IM↗

arXiv2018

A constrained regression model for an ordinal response with ordinal predictors

A regression model is proposed for the analysis of an ordinal response variable depending on a set of multiple covariates containing ordinal and potentially other variables. The proportional odds model (McCullagh (1980)) is used for the ordinal response, and constrained maximum likelihood estimation is used to account for the ordinality of covariates. Ordinal predictors are coded by dummy variables. The parameters associated to the categories of the ordinal predictor(s) are constrained, enforcing them to be monotonic (isotonic or antitonic). A decision rule is introduced for classifying the ordinal predictors' monotonicity directions, also providing information whether observations are compatible with both or no monotonicity direction. In addition, a monotonicity test for the parameters of any ordinal predictor is proposed. The monotonicity constrained model is proposed together with three estimation methods and compared to the unconstrained one based on simulations. The model is applied to real data explaining a 10-Points Likert scale quality of life self-assessment variable from ordinal and other predictors.

stat.ME↗