Dynamically typed programming languages like R allow programmers to write generic, flexible and concise code and to interact with the language using an interactive Read-eval-print-loop (REPL).
Computation
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods.
Methodology
Markov chain Monte Carlo (MCMC) has transformed Bayesian model inference over the past three decades and is now a workhorse of applied scientists.
Methodology Applications
The methods compared are Monte Carlo with pseudo-random numbers, Latin Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol sequences.
Applications Computation
Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous under- standing of why it performs so well on difficult problems and how it is best applied in practice.
Methodology
When properly tuned, Hamiltonian Monte Carlo scales to some of the most challenging high-dimensional problems at the frontiers of applied statistics, but when that tuning is suboptimal the performance leaves much to be desired.
Methodology
Hamiltonian Monte Carlo can provide powerful inference in complex statistical problems, but ultimately its performance is sensitive to various tuning parameters.
Methodology Statistics Theory Statistics Theory
The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model.
Methodology
Standard MMs are static, whereas ODE systems are usually dynamic and account for herd immunity which is crucial to prevent overestimation of infection prevalence.
Methodology
In step 2, a similar procedure to the first step is implemented with the exception that for each bootstrap sample, a subset of covariates is randomly selected with unequal selection probabilities determined by the covariates' importance.
Applications
