Distribution-free strong consistency for nonparametric kernel regression involving nonlinear time series

1997 ◽  
Vol 65 (1) ◽  
pp. 67-86 ◽  
Author(s):  
Zudi Lu ◽  
Ping Cheng
Keyword(s):  
Time Series ◽  
Strong Consistency ◽  
Kernel Regression ◽  
Nonlinear Time Series ◽  
Distribution Free ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Regression

Model-free Asymptotically Best Forecasting of Stationary Economic Time Series

1990 ◽  
Vol 6 (3) ◽  
pp. 348-383 ◽  
Author(s):  
Herman J. Bierens
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Economic Time Series ◽  
Model Free ◽  
Regression Approach ◽  
Data Generating Process ◽  
Economic Time ◽  
Vector Time Series ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Regression

Given observations on a stationary economic vector time series process we show that the best h-step ahead forecast (best in the sense of having minimal mean square forecast error) of one of the variables can be consistently estimated by nonparametric regression on an ARMA memory index. Our approach is based on a combination of the ARMA memory index modeling approach of Bierens [7] with a modification to time series of the nonparametric kernel regression approach of Devroye and Wagner [16]. This approach is truly model-free, as no explicit specification of the distribution of the data generating process is needed.

Nonparametric modeling of mass transfer coefficients for air stripping packed towers

1996 ◽  
Vol 23 (2) ◽  
pp. 549-559 ◽  
Author(s):  
Roberto M. Narbaitz ◽  
Yassine Djebbar
Keyword(s):  
Mass Transfer ◽  
Transfer Coefficient ◽  
Mass Transfer Coefficient ◽  
Kernel Regression ◽  
Transfer Coefficients ◽  
Nonparametric Modeling ◽  
Air Stripping ◽  
Volatile Organic ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Regression

Existing parametric correlations have been found to have difficulties in predicting the removal of trace levels of volatile organic chemicals by modern air stripping towers. In this study, a new approach using a nonparametric kernel regression method was used to predict the mass transfer coefficient, KLa, of air stripping towers. Although only four variables were used, the predictions are already improved more than 50% as compared with Onda correlation, the best existing parametric correlation. The proposed technique shows a dependency of KLa on the liquid flow rate which is in good agreement with established theory. Previous parametric approaches were unable to model this relationship correctly. Key words: mass transfer coefficient, air stripping tower, volatile organic compound, nonparametric kernel regression.

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