44 citations · 126 across the 14 of their papers we have counts for
15 papers
Consensus Planning with Primal, Dual, and Proximal Agents
Alvaro Maggiar, Lee Dicker, Michael Mahoney
Consensus planning is a method for coordinating decision making across complex systems and organizations, including complex supply chain optimization pipelines. It arises when larg…
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Kin G. Olivares, O. Nganba Meetei, Ruijun Ma +3
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are…
The Mahalanobis kernel for heritability estimation in genome-wide association studies: fixed-effects and random-effects methods
Ruijun Ma, Lee H. Dicker
Linear mixed models (LMMs) are widely used for heritability estimation in genome-wide association studies (GWAS). In standard approaches to heritability estimation with LMMs, a gen…
A Local Regret in Nonconvex Online Learning
Sergul Aydore, Lee Dicker, Dean Foster
We consider an online learning process to forecast a sequence of outcomes for nonconvex models. A typical measure to evaluate online learning algorithms is regret but such standard…
Scalable Approximations for Generalized Linear Problems
Murat A. Erdogdu, Mohsen Bayati, Lee H. Dicker
In stochastic optimization, the population risk is generally approximated by the empirical risk. However, in the large-scale setting, minimization of the empirical risk may be comp…
Approximate nonparametric maximum likelihood inference for mixture models via convex optimization
Long Feng, Lee H. Dicker
Nonparametric maximum likelihood (NPML) for mixture models is a technique for estimating mixing distributions that has a long and rich history in statistics going back to the 1950s…
Kernel ridge vs. principal component regression: minimax bounds and adaptability of regularization operators
Lee H. Dicker, Dean P. Foster, Daniel Hsu
Regularization is an essential element of virtually all kernel methods for nonparametric regression problems. A critical factor in the effectiveness of a given kernel method is the…
Ridge regression and asymptotic minimax estimation over spheres of growing dimension
Lee H. Dicker
We study asymptotic minimax problems for estimating a -dimensional regression parameter over spheres of growing dimension (). Assuming that the data follows a linea…
Flexible results for quadratic forms with applications to variance components estimation
Lee H. Dicker, Murat A. Erdogdu
We derive convenient uniform concentration bounds and finite sample multivariate normal approximation results for quadratic forms, then describe some applications involving varianc…
Nonparametric empirical Bayes and maximum likelihood estimation for high-dimensional data analysis
Lee H. Dicker, Sihai D. Zhao
Nonparametric empirical Bayes methods provide a flexible and attractive approach to high-dimensional data analysis. One particularly elegant empirical Bayes methodology, involving…
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Lee H. Dicker
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnost…
Optimal Estimation and Prediction for Dense Signals in High-Dimensional Linear Models
Lee Dicker
Estimation and prediction problems for dense signals are often framed in terms of minimax problems over highly symmetric parameter spaces. In this paper, we study minimax problems…
Parallelism, Uniqueness, and Large-Sample Asymptotics for the Dantzig Selector
Lee Dicker, Xihong Lin
The Dantzig selector (Candes and Tao, 2007) is a popular l1-regularization method for variable selection and estimation in linear regression. We present a very weak geometric condi…
Dense Signals, Linear Estimators, and Out-of-Sample Prediction for High-Dimensional Linear Models
Lee Dicker
Motivated by questions about dense (non-sparse) signals in high-dimensional data analysis, we study the unconditional out-of-sample prediction error (predictive risk) associated wi…
An Alternative Prior Process for Nonparametric Bayesian Clustering
Hanna M. Wallach, Shane T. Jensen, Lee Dicker +1
Prior distributions play a crucial role in Bayesian approaches to clustering. Two commonly-used prior distributions are the Dirichlet and Pitman-Yor processes. In this paper, we in…