I. Wilms

发表

Sparse canonical correlation analysis from a predictive point of view

C. Croux, I. Wilms, 2015, Biometrical journal. Biometrische Zeitschrift.

Commodity Dynamics: A Sparse Multi-Class Approach

C. Croux, I. Wilms, Luca Barbaglia, 2016, 1604.01224.

Forecasting using sparse cointegration

Christophe Croux, Ines Wilms, C. Croux, 2016 .

Volatility spillovers in commodity markets: A large t-vector autoregressive approach

C. Croux, I. Wilms, Luca Barbaglia, 2020 .

A large t-vector autoregressive model for volatility spillover analysis

C. Croux, I. Wilms, Luca Barbaglia, 2017 .

Volatility Spillovers and Heavy Tails: A Large T-Vector Autoregressive Approach

C. Croux, I. Wilms, Luca Barbaglia, 2017, 1708.02073.

An algorithm for the multivariate group lasso with covariance estimation

C. Croux, I. Wilms, C. Croux, 2015, 1512.05153.

An algorithm for the multivariate group lasso with covariance estimation

C. Croux, I. Wilms, 2015, 1512.05153.

Sparse canonical correlation analysis from a predictive point of view

C. Croux, I. Wilms, 2015, Biometrical journal. Biometrische Zeitschrift.

Lasso inference for high-dimensional time series

I. Wilms, Stephan Smeekes, R. Adamek, 2020, Journal of Econometrics.

High Dimensional Forecasting via Interpretable Vector Autoregression

William B. Nicholson, David S. Matteson, Jacob Bien, 2014, J. Mach. Learn. Res..

Multivariate volatility forecasts for stock market indices

C. Croux, J. Rombouts, I. Wilms, 2020 .

White heteroscedasticty testing after outlier removal

I. Wilms, Vanessa Berenguer-Rico, 2018 .

Graphical Influence Diagnostics for Changepoint Models

David S. Matteson, Rebecca Killick, Ines Wilms, 2021, J. Comput. Graph. Stat..

Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages

David S. Matteson, Sumanta Basu, Jacob Bien, 2017, Journal of the American Statistical Association.

Sparse Estimation of Large Time Series Models [R package bigtime version 0.1.0]

David S. Matteson, D. Matteson, J. Bien, 2017 .

The Influence Function of Graphical Lasso Estimators

Germain Van Bever, I. Wilms, Jakob Raymaekers, 2022, 2209.07374.

Local Projection Inference in High Dimensions

I. Wilms, Stephan Smeekes, R. Adamek, 2022, 2209.03218.

Lasso inference for high-dimensional time series

I. Wilms, Stephan Smeekes, R. Adamek, 2020, Journal of Econometrics.

Comparison of estimation methods for cellwise robust regression

C. Croux, I. Wilms, 2016 .

Sparse vector autoregressive models with an application in marketing

Christophe Croux, Sarah Gelper, Ines Wilms, 2015 .

Multi-class Vector AutoRegressive models

Christophe Croux, Luca Barbaglia, Ines Wilms, 2016 .

Sparse estimation of multi-class vector autoregressive models

Christophe Croux, Luca Barbaglia, Ines Wilms, 2017 .

Sparse Vector AutoRegressive Modeling

Christophe Croux, Sarah Gelper, Ines Wilms, 2015 .

Robust variable section with the sparse shooting S

Christophe Croux, Ines Wilms, C. Croux, 2016 .

Principal Balances of Compositional Data for Regression and Classification using Partial Least Squares

Statistics, Vienna, Olomouc, 2022, 2211.01686.

Regularized Predictive Models for Beef Eating Quality of Individual Meals

I. Wilms, G. Tarr, 2022, Data Science in Science.

Robust sparse canonical correlation analysis

Christophe Croux, Ines Wilms, C. Croux, 2015, BMC Systems Biology.

Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

I. Wilms, Alain Hecq, M. Ternes, 2021, J. Comput. Graph. Stat..

Sparse estimation of high-dimensional time series models.

I. Wilms, 2016 .

Sparse Cointegration

C. Croux, I. Wilms, 2014, 1501.01250.

Multi-Class Vector Autoregressive Models for Multi-Store Sales Data

Christophe Croux, Luca Barbaglia, Ines Wilms, 2016, 1605.03325.

The Influence Function of Graphical Lasso Estimators

Germain Van Bever, I. Wilms, Jakob Raymaekers, 2022, Econometrics and Statistics.

Local Projection Inference in High Dimensions

I. Wilms, Stephan Smeekes, R. Adámek, 2022, 2209.03218.

bootUR: An R Package for Bootstrap Unit Root Tests

Stephan Smeekes, Ines Wilms, I. Wilms, 2020, J. Stat. Softw..

Identifying demand effects in a large network of product categories

Christophe Croux, Sarah Gelper, Ines Wilms, 2015, 1506.01589.

Sparse High-Dimensional Vector Autoregressive Bootstrap

I. Wilms, Stephan Smeekes, R. Adámek, 2023, 2302.01233.

High Dimensional Forecasting via Interpretable Vector Autoregression

William B. Nicholson, David S. Matteson, D. Matteson, 2014, J. Mach. Learn. Res..

Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions

I. Wilms, Alain Hecq, Marie Ternes, 2023, 2301.10592.

Tree-based Node Aggregation in Sparse Graphical Models

Jacob Bien, Ines Wilms, J. Bien, 2021, J. Mach. Learn. Res..

Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms

J. Rombouts, I. Wilms, Yujia Hu, 2023, SSRN Electronic Journal.

Robust sparse canonical correlation analysis

C. Croux, I. Wilms, 2015, BMC Systems Biology.

The Predictive Power of the Business and Bank Sentiment of Firms: A High-Dimensional Granger Causality Approach

Christophe Croux, Sarah Gelper, Ines Wilms, 2015, Eur. J. Oper. Res..

Sparse regression for large data sets with outliers

Christophe Croux, Ines Wilms, Lea Bottmer, 2021, Eur. J. Oper. Res..

Multiclass vector auto‐regressive models for multistore sales data

Christophe Croux, Luca Barbaglia, Ines Wilms, 2018 .

Discussion of ‘Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models’

C. Croux, I. Wilms, 2016 .

Heteroscedasticity testing after outlier removal

Vanessa Berenguer-Rico, Ines Wilms, I. Wilms, 2020, Econometric Reviews.

Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages

David S. Matteson, J. Bien, Sumanta Basu, 2017, Journal of the American Statistical Association.

Interpretable vector autoregressions with exogenous time series

David S. Matteson, Sumanta Basu, Jacob Bien, 2017, 1711.03623.