Arbeitspapier
Kernel density estimation for undirected dyadic data
We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables de?ned for all unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by an application of the kernel estimation method of Rosenblatt (1956) and Parzen (1962). We suggest an estimate of their asymptotic variances inspired by a combination of (i) Newey's (1994) method of variance estimation for kernel estimators in the 'monadic' setting and (ii) a variance estimator for the (estimated) density of a simple network ?rst suggested by Holland & Leinhardt (1976). More unusual are the rates of convergence and asymptotic (normal) distributions of our dyadic density estimates. Speci?cally, we show that they converge at the same rate as the (unconditional) dyadic sample mean: the square root of the number, N, of nodes. This di?ers from the results for nonparametric estimation of densities and regres-sion functions for monadic data, which generally have a slower rate of convergence than their corresponding sample mean.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP39/19
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Semiparametric and Nonparametric Methods: General
Estimation: General
- Thema
-
Networks
Dyads
Kernel Density Estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Graham, Bryan S.
Niu, Fengshi
Powell, James
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2019
- DOI
-
doi:10.1920/wp.cem.2019.3919
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Graham, Bryan S.
- Niu, Fengshi
- Powell, James
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2019
Ähnliche Objekte (12)
Kernel density estimation for heaped data
Shape constrained kernel density estimation
Shape constrained kernel density estimation
Nonparametric Kernel Density Estimation Near the Boundary
Nonparametric Kernel density estimation near the boundary
Plug-in bandwidth selection for kernel density estimation with discrete data
Data adaptive kernel regression estimation
A Review of Kernel Density Estimation with Applications to Econometrics
Modified-likelihood estimation of fixed-effect models for dyadic data
Density estimation over data streams
Data-driven deep density estimation
Two sample rank tests with adaptive score functions using kernel density estimation
Kernel density estimation for heaped data
Shape constrained kernel density estimation
Shape constrained kernel density estimation
Nonparametric Kernel Density Estimation Near the Boundary
Nonparametric Kernel density estimation near the boundary
Plug-in bandwidth selection for kernel density estimation with discrete data
Data adaptive kernel regression estimation
A Review of Kernel Density Estimation with Applications to Econometrics
Modified-likelihood estimation of fixed-effect models for dyadic data
Density estimation over data streams
Data-driven deep density estimation
Two sample rank tests with adaptive score functions using kernel density estimation
Kernel density estimation for heaped data
Shape constrained kernel density estimation
Shape constrained kernel density estimation
Nonparametric Kernel Density Estimation Near the Boundary
Nonparametric Kernel density estimation near the boundary
Plug-in bandwidth selection for kernel density estimation with discrete data
Data adaptive kernel regression estimation
A Review of Kernel Density Estimation with Applications to Econometrics
Modified-likelihood estimation of fixed-effect models for dyadic data
Density estimation over data streams
Data-driven deep density estimation