Multivariate kernel density estimators are known to systematically deviate from the true value near critical points of the density surface. To overcome this difficulty a method based on Rao Blackwell's theorem is proposed. Local corrections of kernel density estimators are achieved by conditioning t
β¦ LIBER β¦
On near neighbour estimates of a multivariate density
β Scribed by Peter Hall
- Publisher
- Elsevier Science
- Year
- 1983
- Tongue
- English
- Weight
- 696 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0047-259X
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Pointwise Improvement of Multivariate Ke
β
Belkacem Abdous; Alain Berlinet
π
Article
π
1998
π
Elsevier Science
π
English
β 428 KB
On some multivariate density estimates a
β
Khairia El-Said El-Nadi
π
Article
π
1982
π
Elsevier Science
π
English
β 397 KB
On kernel density estimation near endpoi
β
Shunpu Zhang; Rohana J. Karunamuni
π
Article
π
1998
π
Elsevier Science
π
English
β 144 KB
In this paper, we consider the estimation problem of f(0), the value of density f at the left endpoint 0. Nonparametric estimation of f( 0) is rather formidable due to boundary e ects that occur in nonparametric curve estimation. It is well known that the usual kernel density estimates require modiΓΏ
Nonparametric iterative estimation of mu
β
Wen-Qi Liang; P.R. Krishnaiah
π
Article
π
1985
π
Elsevier Science
π
English
β 481 KB
Estimation of multivariate binary densit
β
X.R. Chen; P.R. Krishnaiah; W.W. Liang
π
Article
π
1989
π
Elsevier Science
π
English
β 355 KB
On the asymptotic distribution of multiv
β
Raymond J Carroll
π
Article
π
1978
π
Elsevier Science
π
English
β 478 KB