𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Marginal density estimation from incomplete bivariate data

✍ Scribed by Martin L. Hazelton

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
145 KB
Volume
47
Category
Article
ISSN
0167-7152

No coin nor oath required. For personal study only.

✦ Synopsis

The problem of estimating a marginal density from incomplete bivariate data is considered. A kernel estimator is proposed. Strong consistency of the estimator is proved, and asymptotic formulae for its mean and variance derived. A method of bandwidth selection is suggested. Application of the estimator is then illustrated on example data sets. Possible extensions and improvements are discussed.

πŸ“œ SIMILAR VOLUMES

Bivariate Density Estimation with Random
Bivariate Density Estimation with Randomly Truncated Data
✍ ÜlkΓΌ GΓΌrler; Kathryn Prewitt πŸ“‚ Article πŸ“… 2000 πŸ› Elsevier Science 🌐 English βš– 220 KB

In this study bivariate kernel density estimators are considered when a component is subject to random truncation. In bivariate truncation models one observes the i.i.d. samples from the triplets (T, Y, X) only if T Y. In this set-up, Y is said to be left truncated by T and T is right truncated by Y

Estimation of Growth Norms from Incomple
Estimation of Growth Norms from Incomplete Longitudinal Data
✍ Prof. Robert F. Woolson; William R. Clarke πŸ“‚ Article πŸ“… 1987 πŸ› John Wiley and Sons 🌐 English βš– 723 KB

Longitudinal atudiea are rarely complete due to attrition, miatimed vieits and observations misaing at random. When the data are missing a t random it L poasible to estimate the primary location parameters of interest by constructing a modification of ZELLNEB'S (1962) seemingly unrelated regression