Related papers: Correcting inferences for volunteer-collected data…
The application of state-of-the-art spatial econometric models requires that the information about the spatial coordinates of statistical units is completely accurate, which is usually the case in the context of areal data. With…
Citizen science databases that consist of volunteer-led sampling efforts of species communities are relied on as essential sources of data in ecology. Summarizing such data across counties with frequentist-valid prediction sets for each…
This article looks at the combined constraints from a photometric and spectroscopic survey. These surveys will measure cosmology using weak lensing (WL), galaxy clustering, baryon acoustic oscillations (BAO) and redshift space distortions…
Future weak lensing surveys will directly probe the density fluctuation in the universe. Recent studies have shown how the statistics of the weak lensing convergence field is related to the statistics of collapsed objects. Extending earlier…
I discuss the effects of measurement error on regression and density estimation. I review the statistical methods that have been developed to correct for measurement error that are most popular in astronomical data analysis, discussing…
This paper explores the topic of preferential sampling, specifically situations where monitoring sites in environmental networks are preferentially located by the designers. This means the data arising from such networks may not accurately…
With a wariness of Occam's razor awakened by the discovery of cosmic acceleration, we abandon the usual assumption of zero mean curvature and ask how well it can be determined by planned surveys. We also explore the impact of uncertain mean…
Spatial prediction of weather-elements like temperature, precipitation, and barometric pressure are generally based on satellite imagery or data collected at ground-stations. None of these data provide information at a more granular or…
In almost any geostatistical analysis, one of the underlying, often implicit, modelling assump- tions is that the spatial locations, where measurements are taken, are recorded without error. In this study we develop geostatistical inference…
Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data--driven image…
Joint gravitational-wave (GW) and $\gamma$-ray burst (GRB) observations are among the best prospects for standard siren cosmology. However, the strong selection effect for the coincident GRB detection, which is possible only for sources…
Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanning point clouds representing different classes of ground…
In the first paper of this series, we studied the effect of baryon acoustic oscillations (BAO), redshift space distortions (RSD) and weak lensing (WL) on measurements of angular cross-correlations in narrow redshift bins. Paper-II presented…
Accurate shape measurements are essential to infer cosmological parameters from large area weak gravitational lensing studies. The compact diffraction-limited point-spread function (PSF) in space-based observations is greatly beneficial,…
Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like those typically exhibited in natural ecosystems. Coordinate…
At third-generation (3G) gravitational-wave detector networks, compact binaries coalescences produce a ``confusion noise'' due to unresolved sources and to the error in the reconstruction of resolved sources, that can degrade the…
Cosmological weak lensing is the powerful probe of cosmology. Here we address one of the most fundamental, statistical questions inherent in weak lensing cosmology: whether or not we can recover the initial Gaussian information content of…
Large-scale, volunteer-collected datasets of community-identified natural world imagery like iNaturalist have enabled marked performance gains for fine-grained visual classification of species using machine learning methods. However, such…
Facing the advent of the next generation cosmological surveys we present a method to forecast knowledge gain on cosmological models. We propose this as a well defined and general tool to quantify the performance of different experiments in…
The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data. After introducing this new family of generative…