Volume 16, Issue 2 (12-2017)                   JIRSS 2017, 16(2): 33-50 | Back to browse issues page

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Chowdhury M, VanBrackle ‎, Patwary M. Two-step Smoothing Estimation of the Time-variant Parameter with Application to Temperature Data. JIRSS. 2017; 16 (2) :33-50
URL: http://jirss.irstat.ir/article-1-348-en.html
Department of Statistics and Analytical Sciences‎, ‎KSU‎, ‎Georgia‎, ‎USA , mchowd10@kennesaw.edu
Abstract:   (4171 Views)

‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time points and then compute the final estimator at any time by smoothing the raw estimators‎. ‎We will call these estimators two-step local polynomial smoothing estimator and two-step kernel smoothing estimator‎. ‎We derive these two two-step smoothing estimators by modeling raw estimates of the time-variant parameter from any regression model or probability model and then establish a mathematical relationship between these two estimators‎. ‎Our two-step estimation method is applied to temperature data from Dhaka‎, ‎the capital city of Bangladesh‎. ‎Extensive simulation studies under different cross-sectional and longitudinal frameworks have been conducted to check the finite sample MSE of our estimators‎. ‎Narrower bootstrap confidence bands and smaller MSEs from application and simulation results show the superiority of the local polynomial smoothing estimator over the kernel smoothing estimator‎. 

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Type of Study: Original Paper | Subject: 62Gxx: Nonparametric inference
Received: 2016/03/26 | Accepted: 2017/02/20 | Published: 2017/02/20

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