Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time.

Applications

We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target distribution.

Methodology Computation 62F15, 62M05, 65C05, 65C40

We find that black drivers are stopped more often than white drivers relative to their share of the driving-age population, but that Hispanic drivers are stopped less often than whites.

Applications

We present on-line algorithms for computing approximations of rank-based statistics that give high accuracy, particularly near the tails of a distribution, with very small sketches.

Sequential Quantile Estimation Computation Data Structures and Algorithms

Just as the position of an object moving through space can be visualized as a 3D trajectory, HyperTools uses dimensionality reduction algorithms to create similar 2D and 3D trajectories for time series of high-dimensional observations.

Other Statistics

This paper discusses desirable properties of forecasting models in production systems.

Methodology

Both Bayesian and varying coefficient models are very useful tools in practice as they can be used to model parameter heterogeneity in a generalizable way.

Applications Methodology

Time series forecasting is an active research topic in academia as well as industry.

Computation Methodology

ruptures is a Python library for offline change point detection.

Computation Mathematical Software

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory.

Methodology