2 citations · 2 across the 5 of their papers we have counts for
5 papers
Iterating marginalized Bayes maps for likelihood maximization with application to nonlinear panel models
Jesse Wheeler, Aaron J. Abkemeier, Edward L. Ionides
Complex dynamic systems can be investigated by fitting mechanistic stochastic dynamic models to time series data. In this context, commonly used Monte Carlo inference procedures fo…
Mechanistic models for panel data: Analysis of ecological experiments with four interacting species
Bo Yang, Jesse Wheeler, Meghan A. Duffy +2
In an ecological context, panel data arise when time series measurements are made on a collection of ecological processes. Each process may correspond to a spatial location for fie…
panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R
Carles Bretó, Jesse Wheeler, Aaron A. King +1
Panel data arise when time series measurements are collected from multiple, dynamically independent but structurally related systems. Each system's time series can be modeled as a…
A tutorial on panel data analysis using partially observed Markov processes via the R package panelPomp
Carles Breto, Jesse Wheeler, Aaron A. King +1
The R package panelPomp supports analysis of panel data via a general class of partially observed Markov process models (PanelPOMP). This package tutorial describes how the mathema…
Revisiting Inference for ARMA Models: Improved Fits and Superior Confidence Intervals
Jesse Wheeler, Edward L. Ionides
Autoregressive moving average (ARMA) models are widely used for analyzing time series data. However, standard likelihood-based inference methodology for ARMA models has avoidable l…