Publications

. Handling Missing Data in Self-exciting Point Process Models. Spatial Statistics, 2019.

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. Elastic Principal Components Regression. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2018.

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. Comparing multiple statistical methods for inverse prediction in nuclear forensics applications. Chemometrics and Intelligent Laboratory Systems, 2018.

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. Selecting an Informative/Discriminating Multivariate Response For Inverse Prediction. Journal of Quality Technology, 2017.

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. Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions. ASME 2017 Pressure Vessels and Piping Conference, 2017.

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. Bayesian Restricted Likelihood Methods. Technical Report No. 878, Department of Statistics, The Ohio State University, 2014.

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. Robust Inference via the Blended Paradigm. JSM Proceedings, 2012.

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Posts

Since starting to use R about a decade ago, I have been a consistent consumer of information from stackoverflow (SO). It is an invaluable resource and I am grateful to those who take the time to ask good questions and provide good answers. I have also felt a little guilty for not giving back by being an active participant. In this post I will give some (mostly bad) reasons why I was not actively participating, what finally pushed me to participate, and some benefits and tips for answering questions on SO.

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Conditioning on robust summaries of the data in a Bayesian model is one way to achieve robustness to model miss-specification. I have called this the “restricted likelihood” here and here since the full data likelihood is replaced with the likelihood conditioned on only the robust summary (i.e. a restricted likelihood). One of the easiest examples to conceptualize is outliers in a univariate setting. Suppose the true data generating mechanism is a contaminated normal:

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The tibble package within the the tidyverse provides ‘a modern take on data frames.’ It’s loaded with nice features, one of which is the ability to store list-columns. List-columns provide a concise way to store lists within a row of a data frame. In particular, this is useful for storing functional data because a common feature in such data is that each function is not collected at the same number of points.

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