This article introduces the R package hermiter which facilitates estimation of univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation coefficients (bivariate) using Hermite series based estimators.
The model is used to estimate case fatality for multiple diseases in the city regions of England, based on incidence, prevalence and mortality data from the Global Burden of Disease study.
Statisticians recommend the Design and Analysis of Experiments (DAE) for evidence-based research but often use tables to present their own simulation studies.
Methodology 62K99 (Primary) 62K25 (Secondary)
In particular, our framework is robust to biased data batches in the sense that the proposed online estimator is asymptotically unbiased as long as the pooled data is a random sample of the target population regardless of whether each data batch is.
We present the learnt harmonic mean estimator, a variant of the original estimator that solves its large variance problem.
Methodology Instrumentation and Methods for Astrophysics Computation
Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable.
In this work we (1) review likelihood-based inference for parameter estimation and the construction of confidence regions, and (2) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping.
Methodology Applications 62-08
In large epidemiologic studies, self-reported outcomes are often used to record disease status more frequently than by gold standard diagnostic tests alone.
Categorizing individual cells into one of many known cell type categories, also known as cell type annotation, is a critical step in the analysis of single-cell genomics data.
The spOccupancy package provides a user-friendly approach to fit a variety of single and multispecies occupancy models, making it straightforward to address detection biases and spatial autocorrelation in species distribution models even for large data sets.