Daniel J. McDonald | DeepAI

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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...

0 Daniel J. McDonald, et al. ∙

∙ 09/19/2022

Exponential Family Trend Filtering on Lattices

Trend filtering is a modern approach to nonparametric regression that is...

0 Veeranjaneyulu Sadhanala, et al. ∙

∙ 08/05/2022

sparsegl: An R Package for Estimating Sparse Group Lasso

The sparse group lasso is a high-dimensional regression technique that i...

0 Xiaoxuan Liang, et al. ∙

∙ 02/20/2022

Smooth multi-period forecasting with application to prediction of COVID-19 cases

Forecasting methodologies have always attracted a lot of attention and h...

0 Elena Tuzhilina, et al. ∙

∙ 07/05/2021

Sufficient principal component regression for pattern discovery in transcriptomic data

Methods for global measurement of transcript abundance such as microarra...

0 Lei Ding, et al. ∙

∙ 07/14/2019

Markov-switching State Space Models for Uncovering Musical Interpretation

For concertgoers, musical interpretation is the most important factor in...

0 Daniel J. McDonald, et al. ∙

∙ 07/12/2019

Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression

Motivation: The discovery of relationships between gene expression measu...

0 Lei Ding, et al. ∙

∙ 05/18/2018

Modeling trend in temperature volatility using generalized LASSO

In this paper, we present methodology for estimating trends in spatio-te...

0 Arash Khodadadi, et al. ∙

∙ 02/04/2016

Risk estimation for high-dimensional lasso regression

In high-dimensional estimation, analysts are faced with more parameters ...

0 Darren Homrighausen, et al. ∙

∙ 02/02/2016

On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets

In this paper we analyze approximate methods for undertaking a principal...

0 Darren Homrighausen, et al. ∙

∙ 08/04/2013

Risk-consistency of cross-validation with lasso-type procedures

The lasso and related sparsity inducing algorithms have been the target ...

0 Darren Homrighausen, et al. ∙

∙ 12/03/2012

Nonparametric risk bounds for time-series forecasting

We derive generalization error bounds for traditional time-series foreca...

0 Daniel J. McDonald, et al. ∙

∙ 11/15/2011

Estimated VC dimension for risk bounds

Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the gener...

0 Daniel J. McDonald, et al. ∙

∙ 07/21/2011

Spectral approximations in machine learning

In many areas of machine learning, it becomes necessary to find the eige...

0 Darren Homrighausen, et al. ∙

∙ 06/03/2011

Rademacher complexity of stationary sequences

We show how to control the generalization error of time series models wh...

0 Daniel J. McDonald, et al. ∙

∙ 03/04/2011

Generalization error bounds for stationary autoregressive models

We derive generalization error bounds for stationary univariate autoregr...

0 Daniel J. McDonald, et al. ∙

∙ 03/04/2011

Estimating β-mixing coefficients

The literature on statistical learning for time series assumes the asymp...

0 Daniel J. McDonald, et al. ∙

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