Nearest neighbor conditional estimation for Harris recurrent Markov chains
From MaRDI portal
Publication:1036785
DOI10.1016/j.jmva.2009.06.013zbMath1175.62089OpenAlexW2054939461MaRDI QIDQ1036785
Publication date: 13 November 2009
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe0735.pdf
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) Markov processes: estimation; hidden Markov models (62M05)
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Consistent estimation of a general nonparametric regression function in time series ⋮ \(k\)NN estimation in functional partial linear modeling ⋮ Conditional estimation for dependent functional data ⋮ Nonparametric estimation of multivariate elliptic densities via finite mixture sieves ⋮ NULL RECURRENT UNIT ROOT PROCESSES ⋮ Stochastic temporal data upscaling using the generalized \(k\)-nearest neighbor algorithm
Cites Work
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- Markov chains and stochastic stability
- Regenerative block-bootstrap for Markov chains
- On the almost everywhere convergence of nonparametric regression function estimates
- Nearest neighbor regression estimation for null-recurrent Markov time series
- Mixing: Properties and examples
- The random difference equation \(X_ n = A_ n X_{n-1} + B_ n\) in the critical case
- How often does a Harris recurrent Markov chain recur?
- A new weak dependence condition and applications to moment inequalities
- On the points of regularity of multivariate functions of bounded variation
- New dependence coefficients. Examples and applications to statistics
- Extremal behaviour of solutions to a stochastic difference equation with applications to ARCH processes
- A uniform CLT for uniformly bounded families of martingale differences
- Dependent central limit theorems and invariance principles
- Nonparametric estimation in null recurrent time series.
- Weak dependence beyond mixing and asymptotics for nonparametric regression
- Weak convergence and empirical processes. With applications to statistics
- Nonparametric regression estimation for dependent functional data: asymptotic normality
- Weak convergence of laws on \(\mathbb R^{K}\) with common marginals
- NONPARAMETRIC ESTIMATORS FOR TIME SERIES
- Weakly Differentiable Functions
- Foundations of Modern Probability
- On Definitions of Bounded Variation for Functions of Two Variables
- Real Analysis and Probability
- WEAK DEPENDENCE: MODELS AND APPLICATIONS TO ECONOMETRICS
- Bootstrap Methods for Markov Processes
