scholarly journals Estimation of Double Smoothing Parameters with Simulation

2004 ◽  
Vol 1 (1) ◽  
pp. 59-74
Author(s):  
Fadel Al-Taie
2008 ◽  
Vol 47 (02) ◽  
pp. 167-173 ◽  
Author(s):  
A. Pfahlberg ◽  
O. Gefeller ◽  
R. Weißbach

Summary Objectives: In oncological studies, the hazard rate can be used to differentiate subgroups of the study population according to their patterns of survival risk over time. Nonparametric curve estimation has been suggested as an exploratory means of revealing such patterns. The decision about the type of smoothing parameter is critical for performance in practice. In this paper, we study data-adaptive smoothing. Methods: A decade ago, the nearest-neighbor bandwidth was introduced for censored data in survival analysis. It is specified by one parameter, namely the number of nearest neighbors. Bandwidth selection in this setting has rarely been investigated, although the heuristical advantages over the frequently-studied fixed bandwidth are quite obvious. The asymptotical relationship between the fixed and the nearest-neighbor bandwidth can be used to generate novel approaches. Results: We develop a new selection algorithm termed double-smoothing for the nearest-neighbor bandwidth in hazard rate estimation. Our approach uses a finite sample approximation of the asymptotical relationship between the fixed and nearest-neighbor bandwidth. By so doing, we identify the nearest-neighbor bandwidth as an additional smoothing step and achieve further data-adaption after fixed bandwidth smoothing. We illustrate the application of the new algorithm in a clinical study and compare the outcome to the traditional fixed bandwidth result, thus demonstrating the practical performance of the technique. Conclusion: The double-smoothing approach enlarges the methodological repertoire for selecting smoothing parameters in nonparametric hazard rate estimation. The slight increase in computational effort is rewarded with a substantial amount of estimation stability, thus demonstrating the benefit of the technique for biostatistical applications.


2021 ◽  
Vol 9 (4) ◽  
pp. 325-337
Author(s):  
Robert Z. Selden ◽  
Lauren N. Butaric ◽  
Kersten Bergstrom ◽  
Dennis Van Gerven

ABSTRACTThe production of three-dimensional (3D) digital meshes of surface and computed tomographic (CT) data has become widespread in morphometric analyses of anthropological and archaeological data. Given that processing methods are not standardized, this leaves questions regarding the comparability of processed and digitally curated 3D datasets. The goal of this study was to identify those processing parameters that result in the most consistent fit between CT-derived meshes and a 3D surface model of the same human mandible. Eight meshes, each using unique thresholding and smoothing parameters, were compared to assess whole-object deviations, deviations along curves, and deviations between specific anatomical features on the surface model when compared with the CT scans using a suite of comparison points. Based on calculated gap distances, the mesh that thresholded at “0” with an applied smoothing technique was found to deviate least from the surface model, although it is not the most biologically accurate. Results have implications for aggregated studies that employ multimodal 3D datasets, and caution is recommended for studies that enlist 3D data from websites and digital repositories, particularly if processing parameters are unknown or derived for studies with different research foci.


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