scholarly journals Limit Law of the Local Linear Estimate of the Conditional Hazard Function for Functional Data

2020 ◽  
Author(s):  
Oussama Bouanani ◽  
Abdelhak Guendouzi ◽  
Souheyla Chemikh
Keyword(s):  
Functional Data ◽  
Hazard Function ◽  
Kernel Method ◽  
Random Variable ◽  
Nonparametric Estimator ◽  
Finite Sample ◽  
Linear Estimate ◽  
Local Linear ◽  
Local Linear Estimate ◽  
Linear Technique

In this work, we treat a prediction problem via the conditional hazard function of a scalar response variable Y given a functional random variable X by using the local linear technique. The main purpose of this paper is to investigate the asymptotic normality of the nonparametric estimator of the conditional hazard function, under some general conditions. A simulation study, conducted to assess finite sample behavior, demonstrates the superiority of our method than the standard kernel method

scholarly journals A Class of Local Linear Estimators with Functional Data

2019 ◽  
pp. 379-391 ◽  
Author(s):  
Sara Leulmi ◽  
Fatiha Messaci
Keyword(s):  
Nonparametric Estimation ◽  
Metric Space ◽  
Functional Data ◽  
Regression Function ◽  
Real Data ◽  
Random Variable ◽  
Data Set ◽  
Response Variable ◽  
Local Linear ◽  
Linear Estimators

We introduce a local linear nonparametric estimation for the generalized regression function of a scalar response variable given a random variable taking values in a semi metric space. We establish a rate of uniform consistency for the proposed estimators. Then, based on a real data set we illustrate the performance of a particular studied estimator with respect to other known estimators

Functional local linear estimate for functional relative-error regression

2017 ◽  
Vol 11 (4) ◽  
pp. 771-789 ◽  
Author(s):  
Abdelkader Chahad ◽  
Larbi Ait-Hennani ◽  
Ali Laksaci
Keyword(s):  
Relative Error ◽  
Linear Estimate ◽  
Local Linear ◽  
Local Linear Estimate

APLIKASI METODE KERNEL DALAM ESTIMASI FUNGSI HAZARD

2005 ◽  
Vol 6 (2) ◽  
pp. 13
Author(s):  
Bambang Avip Priatna Martadiputra
Keyword(s):  
Distribution Function ◽  
Probability Density Function ◽  
Kernel Methods ◽  
Hazard Function ◽  
Kernel Method ◽  
Random Variable ◽  
Nonnegative Random Variable ◽  
Cumulative Hazard ◽  
Cumulative Hazard Function ◽  
Key Word

Let T be a nonnegative random variable representing the lifetimes of individuals in some population. Let f(t) denote the probability density function of T and F(t) denote the distribution function of T, the hazard function of T defined as  F(t) - 1  S(t)   whereS(t) f(t) h(t)   If equation (1) integrated we have cumulative hazard function H (t).  This paper describes application of kernel method for estimation of hazard function h (.) based censoring data. And then we will show that the hazard estimator is unbiased asymptotically, consistent, and normal asymptotically. Key word: kernel methods, estimation hazard function.

scholarly journals Basis Expansions for Functional Snippets

Biometrika ◽  
2020 ◽  
Author(s):  
Zhenhua Lin ◽  
Jane-Ling Wang ◽  
Qixian Zhong
Keyword(s):  
Data Analysis ◽  
Convergence Rate ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Covariance Function ◽  
Covariance Estimation ◽  
Simulation Studies ◽  
Finite Sample ◽  
Covariance Functions ◽  
The Mean

Summary Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean and covariance functions. In this paper, we investigate mean and covariance estimation for functional snippets in which observations from a subject are available only in an interval of length strictly (and often much) shorter than the length of the whole interval of interest. For such a sampling plan, no data is available for direct estimation of the off-diagonal region of the covariance function. We tackle this challenge via a basis representation of the covariance function. The proposed estimator enjoys a convergence rate that is adaptive to the smoothness of the underlying covariance function, and has superior finite-sample performance in simulation studies.

Functional data: local linear estimation of the conditional density and its application

Statistics ◽  
2013 ◽  
Vol 47 (1) ◽  
pp. 26-44 ◽  
Author(s):  
Jacques Demongeot ◽  
Ali Laksaci ◽  
Fethi Madani ◽  
Mustapha Rachdi
Keyword(s):  
Functional Data ◽  
Conditional Density ◽  
Linear Estimation ◽  
Local Linear ◽  
Local Linear Estimation
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