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Generalized Kernel Regression Estimator for Dependent Size-Biased Data


Generalized Kernel Regression Estimator for Dependent Size-Biased Data

Chaubey, Yogendra P., Laib, Naâmane and Li, Jun (2011) Generalized Kernel Regression Estimator for Dependent Size-Biased Data. Journal of Statistical Planning and Inference, 142 (3). pp. 708-727. ISSN 0378-3758

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Generalized_Kernel_Weight_Regression_Size_Biased_Data_-_Copie.pdf - Submitted Version

Official URL: http://dx.doi.org/10.1016/j.jspi.2011.09.008


This paper considers nonparametric regression estimation in the context of dependent biased non-negative data using a generalized asymmetric kernel. It may be applied to a wider variety of practical situations, such as the length and size biased data. We derive theoretical results using a deep asymptotic analysis of the behavior of the estimator that provides consistency and asymptotic normality in addition to the evaluation of the asymptotic bias term. The asymptotic mean squared error is also derived in order to obtain the optimal value of smoothing parameters required in the proposed estimator. The results are stated under a stationary ergodic assumption, without assuming any traditional mixing conditions. A simulation study is carried out to compare the proposed estimator with the local linear regression estimate.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Article
Authors:Chaubey, Yogendra P. and Laib, Naâmane and Li, Jun
Journal or Publication:Journal of Statistical Planning and Inference
Date:September 2011
  • NSERC, Concordia
Digital Object Identifier (DOI):10.1016/j.jspi.2011.09.008
Keywords:Ergodic process, Gamma density function, Length biased data, Martingale difference, Mixing, MSE, Normality, Regression function
ID Code:973581
Deposited On:06 Feb 2012 18:37
Last Modified:18 Jan 2018 17:36
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