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.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP39/19
- Classification
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Wirtschaft
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Semiparametric and Nonparametric Methods: General
Estimation: General
- Subject
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Networks
Dyads
Kernel Density Estimation
- Event
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Geistige Schöpfung
- (who)
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Graham, Bryan S.
Niu, Fengshi
Powell, James
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2019
- DOI
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doi:10.1920/wp.cem.2019.3919
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Graham, Bryan S.
- Niu, Fengshi
- Powell, James
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2019
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