The package fnets for the R language implements the suite of methodologies proposed by Barigozzi et al. (2022) for the network estimation and forecasting of high-dimensional time series under a factor-adjusted vector autoregressive model, which permits strong spatial and temporal correlations in the data.
In all three cases, we derive non-asymptotic bounds for the accuracy of the DCDP change point estimators.
In this paper, we explore the class of the Hidden Semi-Markov Model (HSMM), a flexible extension of the popular Hidden Markov Model (HMM) that allows the underlying stochastic process to be a semi-Markov chain.
Applications Econometrics Computation
We show that usage of differential ridge penalties for covariate groups may enhance prediction accuracy, while calibration and coverage benefit from additional shrinkage of the penalties.
We propose a Bayesian, noisy-input, spatial-temporal generalised additive model to examine regional relative sea-level (RSL) changes over time.
The analytic results did not suggest that less time spent on SB and more in PA was associated with better cognitive function.
Existing large-sample results show that both specialized and generic methods are applicable to models of serially-dependent data.
In Maples et al. (2018) we introduced Robust Chauvenet Outlier Rejection, or RCR, a novel outlier rejection technique that evolves Chauvenet's Criterion by sequentially applying different measures of central tendency and empirically determining the rejective sigma value.
Computation Instrumentation and Methods for Astrophysics
Principal stratification is a framework for causal inference in the presence of intercurrent events.