Monte Carlo experiment to determine the statistical uncertainty for the avergae 24-hour wave derived from filtered and unfiltered data

1993 ◽  
pp. 416-420
Author(s):  
Scott E. Forbush ◽  
S. P. Duggal ◽  
Martin A. Pomerantz
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Experiment ◽  
Statistical Uncertainty ◽  
Unfiltered Data

Monte Carlo experiment to determine the statistical uncertainty for the average 24-hour wave derived from filtered and unfiltered data

1968 ◽  
Vol 46 (10) ◽  
pp. S985-S989 ◽  
Author(s):  
Scott E. Forbush ◽  
S. P. Duggal ◽  
Martin A. Pomerantz
Keyword(s):  
Monte Carlo ◽  
Diurnal Variation ◽  
Harmonic Analysis ◽  
Random Noise ◽  
Monte Carlo Experiment ◽  
Statistical Uncertainty ◽  
Before And After ◽  
Unfiltered Data

To test whether the diurnal variation is more reliably determined from filtered data, a daily harmonic analysis is made before and after filtering an adequate sequence of synthetic bihourly values containing only random noise and a 24-hour wave of constant phase and amplitude. For each of three filters it is shown, empirically, that the statistical uncertainty of the 24-hour wave from N days of such filtered data does not differ significantly from that from N days of unfiltered data. The filters were of different bandwidths and each was designed to pass the 24-hour wave.

Filtering with ARCH: A Monte Carlo Experiment

1996 ◽  
Author(s):  
Daniel B. Nelson ◽  
Boaz A. Schwartz
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Experiment

scholarly journals NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA

2002 ◽  
Vol 18 (2) ◽  
pp. 420-468 ◽  
Author(s):  
Oliver Linton ◽  
Yoon-Jae Whang
Keyword(s):  
Monte Carlo ◽  
Characteristic Function ◽  
Asymptotic Normality ◽  
Nonparametric Estimation ◽  
Regression Function ◽  
Rates Of Convergence ◽  
Monte Carlo Experiment ◽  
Aggregated Data ◽  
Practical Performance ◽  
Consistency And Asymptotic Normality

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intrafamily component but require that observations from different families be independent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behavior of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.

Power of Tests for Nonlinear Transformation in Regression Analysis

1994 ◽  
Vol 10 (2) ◽  
pp. 357-371 ◽  
Author(s):  
Masahito Kobayashi
Keyword(s):  
Monte Carlo ◽  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Alternative Hypothesis ◽  
Nonlinear Transformation ◽  
Monte Carlo Experiment ◽  
Local Power ◽  
Power Of Tests ◽  
Extended Projection

This paper compares the local power of tests for a nonlinear transformation of the dependent variable in a regression model against the alternative hypothesis of a linear transformation. It is shown that the local power of the Cox test is higher than those of the extended projection test of MacKinnon, White, and Davidson, and Bera and McAleer's test. The theoretical result is supported by a Monte-Carlo experiment in testing for a regression model with a logarithmically transformed dependent variable against a linear regression model.

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