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STRONG UNIFORM CONSISTENCY RATES OF CONDITIONAL DENSITY ESTIMATION IN THE SINGLE FUNCTIONAL INDEX MODEL FOR FUNCTIONAL DATA UNDER RANDOM CENSORSHIP
被引:0
作者:
Kadiri, Nadia
[1
]
Meghnafi, Mustapha
[2
]
Rabhi, Abbes
[3
]
机构:
[1] Univ Djillali LIABES Sidi Bel Abbes, Sidi Bel Abbes, Algeria
[2] Univ Bechar, Fac Exact Sci, Dept Math, Bechar, Algeria
[3] Univ Djillali Liabes Sidi Bel Abbes, Lab Math, Sidi Bel Abbes, Algeria
关键词:
conditional density;
functional single-index process;
functional random variable;
nonparametric estimation;
small ball probability;
NONPARAMETRIC-ESTIMATION;
ASYMPTOTIC NORMALITY;
LAWS;
D O I:
暂无
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
The main objective of this paper is to investigate the estimation of conditional density function based on the single-index model in the censorship model when the sample is considered as an inde-pendent and identically distributed (i.i.d.) random variables. First of all, a kernel type estimator for the conditional density function (cond-df) is introduced. Afterwards, the asymptotic properties are stated when the observations are linked with a single-index structure. The pointwise almost com-plete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. As an application the conditional mode in functional single-index model is presented. Finally, a simulation study is carried out to evaluate the performance of this estimate.
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页码:221 / 249
页数:29
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