Latest Research
fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling
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.
Computation 62-04
Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions
In all three cases, we derive non-asymptotic bounds for the accuracy of the DCDP change point estimators.
Methodology
Sequential Bayesian Learning for Hidden Semi-Markov Models
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
Think before you shrink: Alternatives to default shrinkage methods can improve prediction accuracy, calibration and coverage
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.
Methodology Applications
A noisy-input generalised additive model for relative sea-level change along the Atlantic coast of North America
We propose a Bayesian, noisy-input, spatial-temporal generalised additive model to examine regional relative sea-level (RSL) changes over time.
Applications Methodology
Scalable Gaussian Process Inference with Stan
Gaussian processes (GPs) are sophisticated distributions to model functional data.
Methodology Computation
Analysis of the 24-Hour Activity Cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought (ACT) Study
The analytic results did not suggest that less time spent on SB and more in PA was associated with better cognitive function.
Applications
Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
kuperov/arx
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Existing large-sample results show that both specialized and generic methods are applicable to models of serially-dependent data.
Methodology
Robust Chauvenet Rejection: Powerful, but Easy to Use Outlier Detection for Heavily Contaminated Data Sets
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 with Time-to-Event Outcomes
Principal stratification is a framework for causal inference in the presence of intercurrent events.
Methodology Applications
