Wilks' theorem for semiparametric regressions with weakly dependent data

DOI10.1214/21-AOS2081zbMath1486.62239arXiv2006.06350OpenAlexW4200054434MaRDI QIDQ2073705

Marie Du Roy De Chaumaray, Valentin Patilea, Matthieu Marbac

Publication date: 7 February 2022

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.06350

zbMATH Keywords

kernel smoothingnonlinear time seriesparametric inferencenuisance functionpivotal statistic\( \alpha \)-mixing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Uses Software

• dlnm

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimation and testing for partially linear single-index models
• Empirical likelihood methods with weakly dependent processes
• Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood
• Marginal empirical likelihood and sure independence feature screening
• A review of empirical likelihood methods for time series
• Estimating function approach for CHARN models
• A review on empirical likelihood methods for regression
• Mixing properties of ARCH and time-varying ARCH processes
• Empirical likelihood inference in partially linear single-index models for longitudinal data
• Propriétés de mélange des processus autorégressifs polynomiaux. (Mixing properties of polynomial autoregressive processes)
• Empirical likelihood inference for partial linear models under martingale difference sequence
• A note on the geometric ergodicity of a nonlinear AR-ARCH model
• Basic properties of strong mixing conditions. A survey and some open questions
• Empirical likelihood for heteroscedastic partially linear models
• Extending the scope of empirical likelihood
• Mixing properties of ARMA processes
• Profile likelihood and conditionally parametric models
• Nonparametric vector autoregression
• Empirical likelihood for partial linear models with fixed designs
• Empirical likelihood and general estimating equations
• Empirical likelihood for partial linear models
• Two-step semiparametric empirical likelihood inference
• Bias-corrected empirical likelihood in a multi-link semiparametric model
• High dimensional generalized empirical likelihood for moment restrictions with dependent data
• Semi-parametric estimation of partially linear single-index models
• Empirical likelihood for single-index models
• Empirical likelihood inference for semiparametric model with linear process errors
• Non-linear time series and Markov chains
• Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality
• Empirical Likelihood Confidence Regions in a Partially Linear Single-Index Model
• Empirical Likelihood Semiparametric Regression Analysis for Longitudinal Data
• UNIFORM CONVERGENCE RATES FOR KERNEL ESTIMATION WITH DEPENDENT DATA
• Empirical likelihood ratio confidence intervals for a single functional
• A Review of Nonparametric Time Series Analysis
• Generalized Partially Linear Single-Index Models
• Statistical analysis for multiplicatively modulated nonlinear autoregressive model and its applications to electrophysiological signal analysis in humans
• On extended partially linear single-index models
• Doubly Robust and Efficient Estimators for Heteroscedastic Partially Linear Single-Index Models Allowing high Dimensional Covariates
• Variance Function Partially Linear Single-Index Models
• Towards a Unified Approach for Proving Geometric Ergodicity and Mixing Properties of Nonlinear Autoregressive Processes
• High-dimensional empirical likelihood inference
• \(L_1\) geometric ergodicity of a multivariate nonlinear AR model with an ARCH term.
• Estimation for single-index and partially linear single-index integrated models

This page was built for publication: Wilks' theorem for semiparametric regressions with weakly dependent data