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10/28/2022
Empirical Macroeconomics and DSGE Modeling in Statistical Perspective
Dynamic stochastic general equilibrium (DSGE) models have been an ubiqui...
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05/27/2022
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
Bayesian adaptive experimental design is a form of active learning, whic...
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03/17/2022
Evaluating Posterior Distributions by Selectively Breeding Prior Samples
Using Markov chain Monte Carlo to sample from posterior distributions wa...
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11/17/2021
A Note on Simulation-Based Inference by Matching Random Features
We can, and should, do statistical inference on simulation models by adj...
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12/06/2019
Conditional Mutual Information Estimation for Mixed Discrete and Continuous Variables with Nearest Neighbors
Fields like public health, public policy, and social science often want ...
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11/08/2017
Bootstrapping Generalization Error Bounds for Time Series
We consider the problem of finding confidence intervals for the risk of ...
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11/06/2017
Consistency of Maximum Likelihood for Continuous-Space Network Models
Network analysis needs tools to infer distributions over graphs of arbit...
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11/02/2017
Bootstrapping Exchangeable Random Graphs
We introduce two new bootstraps for exchangeable random graphs. One, the...
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06/08/2015
The LICORS Cabinet: Nonparametric Algorithms for Spatio-temporal Prediction
Spatio-temporal data is intrinsically high dimensional, so unsupervised ...
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09/19/2013
Predictive PAC Learning and Process Decompositions
We informally call a stochastic process learnable if it admits a general...
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12/03/2012
Nonparametric risk bounds for time-series forecasting
We derive generalization error bounds for traditional time-series foreca...
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11/15/2012
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-d...
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07/17/2012
Model Selection for Degree-corrected Block Models
The proliferation of models for networks raises challenging problems of ...
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11/15/2011
Estimated VC dimension for risk bounds
Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the gener...
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06/03/2011
Rademacher complexity of stationary sequences
We show how to control the generalization error of time series models wh...
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03/04/2011
Adapting to Non-stationarity with Growing Expert Ensembles
When dealing with time series with complex non-stationarities, low retro...
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03/04/2011
Generalization error bounds for stationary autoregressive models
We derive generalization error bounds for stationary univariate autoregr...
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03/04/2011
Estimating β-mixing coefficients
The literature on statistical learning for time series assumes the asymp...
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12/30/2009
The Computational Structure of Spike Trains
Neurons perform computations, and convey the results of those computatio...
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01/29/2000