Related papers: Comparison between different methods of model sele…
Cross-validation assesses the predictive ability of a model, allowing one to rank models accordingly. Although the nonparametric bootstrap is almost always used to assess the variability of a parameter, it can be used as the basis for…
The Bayesian and Akaike information criteria aim at finding a good balance between under- and over-fitting. They are extensively used every day by practitioners. Yet we contend they suffer from at least two afflictions: their penalty…
In this paper, we use an unprecedentedly large sample (158) of confirmed strong lens systems for model selection, comparing five well studied Friedmann-Robertson-Walker cosmologies: LCDM, wCDM (the standard model with a variable dark-energy…
We apply a new non-parametric Bayesian method for reconstructing the evolution history of the equation-of-state $w$ of dark energy, based on applying a correlated prior for $w(z)$, to a collection of cosmological data. We combine the latest…
Recently H(z) data obtained from differential ages of galaxies have been proposed as a new geometrical probe of dark energy. In this paper we use those data, combined with other background tests (CMB shift and SNIa data), to constrain a set…
We consider prediction in multiple studies with potential differences in the relationships between predictors and outcomes. Our objective is to integrate data from multiple studies to develop prediction models for unseen studies. We propose…
Approximate Bayesian computation (ABC) is a class of algorithmic methods in Bayesian inference using statistical summaries and computer simulations. ABC has become popular in evolutionary genetics and in other branches of biology. However…
The Akaike information criterion (AIC) is commonly used to select a logistic regression model for optimal prediction of a binary response by a specified family of models. It however lacks a convincing method of prescribing a proper family…
In this study we combine the background and the growth rate data in order to study the ability of the two oscillating dark energy parameterizations, to fit the observational data. Using the likelihood and MCMC method we try to explore the…
Dark energy is one of the deepest puzzles in modern cosmology, and mounting evidence suggests that it is not just a cosmological constant but a genuinely dynamical component. Although cosmology and dynamical systems theory emerged from…
Unmeasured covariates constitute one of the important problems in causal inference. Even if there are some unmeasured covariates, some instrumental variable methods such as a two-stage residual inclusion (2SRI) estimator, or a…
In view of the new Dark Energy Spectroscopic Instrument (DESI) 2025 results, we analyze three types of \emph{Pad\'e cosmology}, based on rational series making use of Pad\'e approximants over the equations of state, namely Pad\'e$^{\omega}$…
Running vacuum models and viscous dark matter scenarios beyond perfect fluid idealization are two appealing theoretical strategies that have been separately studied as alternatives to solve some problems rooted in the $\Lambda$CDM…
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we…
Some remarkable examples of alternative cosmological theories are reviewed here, ranging from a compilation of variations on the Standard Model through the more distant quasi-steady-state cosmology, plasma cosmology, or universe models as a…
Information criteria, such as Akaike's information criterion and Bayesian information criterion are often applied in model selection. However, their asymptotic behaviors for selecting geostatistical regression models have not been well…
We evaluate the statistical significance of the DAMA/LIBRA claims for annual modulation using three independent model comparison techniques, viz frequentist, information theory, and Bayesian analysis. We fit the data from the DAMA/LIBRA…
One of the main goals of physical cosmology is to reconstruct the expansion history of the universe and finding the actual model of dark energy. In this article I review the difficulties of understanding dark energy and discuss about two…
We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a biascorrected adjustment of within-sample error. We focus on…
This paper compares three approaches to the problem of selecting among probability models to fit data (1) use of statistical criteria such as Akaike's information criterion and Schwarz's "Bayesian information criterion," (2) maximization of…