Top Papers
Capitalising on Opportunistic Data for Monitoring Species Relative Abundances
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
Multilevel Delayed Acceptance MCMC
pymc-devs/pymc
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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
A large-scale analysis of racial disparities in police stops across the United States
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
Computing Extremely Accurate Quantiles Using t-Digests
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
Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling
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
Orbit: Probabilistic Forecast with Exponential Smoothing
Time series forecasting is an active research topic in academia as well as industry.
Computation Methodology
HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data
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
A flexible forecasting model for production systems
This paper discusses desirable properties of forecasting models in production systems.
Methodology
ruptures: change point detection in Python
ruptures is a Python library for offline change point detection.
Computation Mathematical Software
Bambi: A simple interface for fitting Bayesian linear models in Python
The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications.
Computation
