The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
NONPARAMETRIC ESTIMATORS FOR TIME SERIES
Abstract
Abstract.Kernel multivariate probability density and regression estimators are applied to a univariate strictly stationary time series Xr We consider estimators of the joint probability density of Xt at different t‐values, of conditional probability densities, and of the conditional expectation of functionals of Xv given past behaviour. The methods seem of particular relevance in light of recent interest in non‐Gaussian time series models. Under a strong mixing condition multivariate central limit theorems for estimators at distinct points are established, the asymptotic distributions being of the same nature as those which would derive from independent multivariate observations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Nadaraya, E. A. (1964) · Theory of Probability & Its Applications · doi:10.1137/1109020 · Cited by 3,462
Cited by
Gallant, A. R., Rossi, P. E., & Tauchen, G. (1992) · Review of Financial Studies · doi:10.1093/rfs/5.2.199 · Cited by 1,295
Nonlinear system theory: Another look at dependence
Wu, W. B. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506715102 · Cited by 572
The specification of conditional expectations
Harvey, C. R. (2001) · Journal of Empirical Finance · doi:10.1016/s0927-5398(01)00036-6 · Cited by 382
Socio‐economic distance and spatial patterns in unemployment
Conley, T. G., & Topa, G. (2002) · Journal of Applied Econometrics · doi:10.1002/jae.670 · Cited by 369
The econometric analysis of models with risk terms
Pagan, A., & Ullah, A. (1988) · Journal of Applied Econometrics · doi:10.1002/jae.3950030202 · Cited by 342
Metadata
- Authors
- Robinson, P. M.
- Venue
- Journal of Time Series Analysis
- Publisher
- Wiley
- Published
- 1983-05
- Volume
- 4
- Issue
- 3
- Pages
- 185-207
- ISSN
- 0143-9782 (ISSN-L)
- 1467-9892
- OpenAlex
- W2023140731
- Cited by
- 562
- References
- 1
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: NONPARAMETRIC ESTIMATORS FOR TIME SERIES. https://app.cochat.ai/papers/10.1111/j.1467-9892.1983.tb00368.x
- The paper
- Robinson, P. M. (1983). NONPARAMETRIC ESTIMATORS FOR TIME SERIES. Journal of Time Series Analysis. https://doi.org/10.1111/j.1467-9892.1983.tb00368.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
