North Carolina State University 
Website :www4.stat.ncsu.edu/~rmartin 

Bio/Interests :
Associate Professor, Statistics, North Carolina State University. Research interests include empirical Bayes, foundations of statistics, highdimensional inference, and mixture models.


rgmarti3@ncsu.edu  
statsmartin 
This article describes our motivation behind the development of RESEARCHERS.ONE, our mission, and how the new platform will fulfull this mission. We also compare our approach with other recent reform initiatives such as postpublication peer review and open access publications.
An inferential model encodes the data analyst's degrees of belief about an unknown quantity of interest based on the observed data, posited statistical model, etc. Inferences drawn based on these degrees of belief should be reliable in a certain sense, so we require the inferential model to be valid. The construction of valid inferential models based on individual pieces of data is relatively straightforward, but how to combine these so that the validity property is preserved? In this paper we analyze some common combination rules with respect to this question, and we conclude that the best strategy currently available is one that combines via a certain dimension reduction step before the inferential model construction.
Often the primary goal of fitting a regression model is prediction, but the majority of work in recent years focuses on inference tasks, such as estimation and feature selection. In this paper we adopt the familiar sparse, highdimensional linear regression model but focus on the task of prediction. In particular, we consider a new empirical Bayes framework that uses the data to appropriately center the prior distribution for the nonzero regression coefficients, and we investigate the method's theoretical and numerical performance in the context of prediction. We show that, in certain settings, the asymptotic posterior concentration in metrics relevant to prediction quality is very fast, and we establish a Bernsteinvon Mises theorem which ensures that the derived prediction intervals achieve the target coverage probability. Numerical results complement the asymptotic theory, showing that, in addition to having strong finitesample performance in terms of prediction accuracy and uncertainty quantification, the computation time is considerably faster compared to existing Bayesian methods.
Statistics has made tremendous advances since the times of Fisher, Neyman, Jeffreys, and others, but the fundamental questions about probability and inference that puzzled our founding fathers still exist and might even be more relevant today. To overcome these challenges, I propose to look beyond the two dominating schools of thought and ask what do scientists need out of statistics, do the existing frameworks meet these needs, and, if not, how to fill the void? To the first question, I contend that scientists seek to convert their data, posited statistical model, etc., into calibrated degrees of belief about quantities of interest. To the second question, I argue that any framework that returns additive beliefs, i.e., probabilities, necessarily suffers from false confidencecertain false hypotheses tend to be assigned high probabilityand, therefore, risks making systematically misleading conclusions. This reveals the fundamental importance of nonadditive beliefs in the context of statistical inference. But nonadditivity alone is not enough so, to the third question, I offer a sufficient condition, called validity, for avoiding false confidence, and present a framework, based on random sets and belief functions, that provably meets this condition.
Bayesian methods provide a natural means for uncertainty quantification, that is, credible sets can be easily obtained from the posterior distribution. But is this uncertainty quantification valid in the sense that the posterior credible sets attain the nominal frequentist coverage probability? This paper investigates the validity of posterior uncertainty quantification based on a class of empirical priors in the sparse normal mean model. We prove that there are scenarios in which the empirical Bayes method provides valid uncertainty quantification while other methods may not, and finitesample simulations confirm the asymptotic findings.
Nonparametric estimation of a mixing density based on observations from the corresponding mixture is a challenging statistical problem. This paper surveys the literature on a fast, recursive estimator based on the predictive recursion algorithm. After introducing the algorithm and giving a few examples, I summarize the available asymptotic convergence theory, describe an important semiparametric extension, and highlight two interesting applications. I conclude with a discussion of several recent developments in this area and some open problems.
This article describes how the filtering role played by peer review may actually be harmful rather than helpful to the quality of the scientific literature. We argue that, instead of trying to filter out the lowquality research, as is done by traditional journals, a better strategy is to let everything through but with an acknowledgment of the uncertain quality of what is published, as is done on the RESEARCHERS.ONE platform. We refer to this as "scholarly mithridatism." When researchers approach what they read with doubt rather than blind trust, they are more likely to identify errors, which protects the scientific community from the dangerous effects of error propagation, making the literature stronger rather than more fragile.
Accurate estimation of valueatrisk (VaR) and assessment of associated uncertainty is crucial for both insurers and regulators, particularly in Europe. Existing approaches link data and VaR indirectly by first linking data to the parameter of a probability model, and then expressing VaR as a function of that parameter. This indirect approach exposes the insurer to model misspecification bias or estimation inefficiency, depending on whether the parameter is finite or infinitedimensional. In this paper, we link data and VaR directly via what we call a discrepancy function, and this leads naturally to a Gibbs posterior distribution for VaR that does not suffer from the aforementioned biases and inefficiencies. Asymptotic consistency and rootn concentration rate of the Gibbs posterior are established, and simulations highlight its superior finitesample performance compared to other approaches.
In a Bayesian context, prior specification for inference on monotone densities is conceptually straightforward, but proving posterior convergence theorems is complicated by the fact that desirable prior concentration properties often are not satisfied. In this paper, I first develop a new prior designed specifically to satisfy an empirical version of the prior concentration property, and then I give sufficient conditions on the prior inputs such that the corresponding empirical Bayes posterior concentrates around the true monotone density at nearly the optimal minimax rate. Numerical illustrations also reveal the practical benefits of the proposed empirical Bayes approach compared to Dirichlet process mixtures.
Publication of scientific research all but requires a supporting statistical analysis, anointing statisticians the de facto gatekeepers of modern scientific discovery. While the potential of statistics for providing scientific insights is undeniable, there is a crisis in the scientific community due to poor statistical practice. Unfortunately, widespread calls to action have not been effective, in part because of statisticians’ tendency to make statistics appear simple. We argue that statistics can meet the needs of science only by empowering scientists to make sound judgments that account for both the nuances of the application and the inherent complexity of funda mental effective statistical practice. In particular, we emphasize a set of statistical principles that scientists can adapt to their everexpanding scope of problems.
Whether the predictions put forth prior to the 2016 U.S. presidential election were right or wrong is a question that led to much debate. But rather than focusing on right or wrong, we analyze the 2016 predictions with respect to a core set of {\em effectiveness principles}, and conclude that they were ineffective in conveying the uncertainty behind their assessments. Along the way, we extract key insights that will help to avoid, in future elections, the systematic errors that lead to overly precise and overconfident predictions in 2016. Specifically, we highlight shortcomings of the classical interpretations of probability and its communication in the form of predictions, and present an alternative approach with two important features. First, our recommended predictions are safer in that they come with certain guarantees on the probability of an erroneous prediction; second, our approach easily and naturally reflects the (possibly substantial) uncertainty about the model by outputting plausibilities instead of probabilities.