Genetic and genomic approaches have implicated hundreds of genetic loci in
neurodevelopmental disorders and neurodegeneration, but mechanistic understanding …

Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

Soil bacteria and fungi play key roles in the functioning of terrestrial ecosystems, yet our
understanding of their responses to climate change lags significantly behind that of other …

A valuable overview of the most important ideas and results in statistical modeling Written by
a highly-experienced author, Foundations of Linear and Generalized Linear Models is a …

Weighting methods that adjust for observed covariates, such as inverse probability
weighting, are widely used for causal inference and estimation with incomplete outcome …

A number of problems in probability and statistics can be addressed using the multivariate
normal (Gaussian) distribution. In the one-dimensional case, computing the probability for a …

This article challenges Fixed Effects (FE) modeling as the 'default'for time-series-cross-
sectional and panel data. Understanding different within and between effects is crucial when …

Many good resources are available with motivation and explanations about online
controlled experiments (Kohavi et al. 2009a, 2020; Thomke 2020; Luca and Bazerman …

Children learn effortlessly by example and exhibit a remarkable capacity of generalization.
The field of machine learning, on the other hand, stumbles along clumsily in search of …

Global climate change and other anthropogenic stressors have heightened the need to
rapidly characterize ecological changes in marine benthic communities across large scales …