Regularised forecasting via smooth-rough partitioning of the regression coefficients
DOI10.1214/19-EJS1573zbMath1425.62126OpenAlexW2944867765WikidataQ127677827 ScholiaQ127677827MaRDI QIDQ2002584
Piotr Fryzlewicz, Hyeyoung Maeng
Publication date: 12 July 2019
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ejs/1561168839
Inference from stochastic processes and prediction (62M20) Numerical computation using splines (65D07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05) Applications of statistics to environmental and related topics (62P12) Sequential estimation (62L12)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Polynomial spline estimation for partial functional linear regression models
- Spline estimators for semi-functional linear model
- A functional linear model for time series prediction with exogenous variables
- On prediction rate in partial functional linear regression
- Functional data analysis for volatility
- A partitioned single functional index model
- Functional linear regression that's interpretable
- Prediction in functional linear regression
- Fractals with point impact in functional linear regression
- Smoothing splines estimators for functional linear regression
- Smoothing splines estimators in functional linear regression with errors-in-variables
- Estimating the dimension of a model
- The functional nonparametric model and applications to spectrometric data
- Linear processes in function spaces. Theory and applications
- Partial functional linear regression
- Nonparametric time series prediction: A semi-functional partial linear modeling
- Functional data analysis.
- Testing stationarity of functional time series
- Partially functional linear regression in high dimensions
- Most-predictive design points for functional data predictors
- A Functional Wavelet–Kernel Approach for Time Series Prediction
- NONPARAMETRIC ESTIMATION OF THE DIFFUSION COEFFICIENT OF STOCHASTIC VOLATILITY MODELS
- Jackknife Empirical Likelihood Test for Equality of Two High Dimensional Means
- Estimation of the Mean of Functional Time Series and a Two-Sample Problem
- NONPARAMETRIC FILTERING OF THE REALIZED SPOT VOLATILITY: A KERNEL-BASED APPROACH
- Tests for Error Correlation in the Functional Linear Model
- On the Prediction of Stationary Functional Time Series
- Truncated Linear Models for Functional Data
- Modelling Function-Valued Stochastic Processes, with Applications to Fertility Dynamics
- Fully Nonparametric Estimation of Scalar Diffusion Models
- Optimal Designs for Longitudinal and Functional Data
- Structured Functional Additive Regression in Reproducing Kernel Hilbert Spaces
- Functional linear regression with points of impact
- Methods for Scalar‐on‐Function Regression