| DMCE {CircOutlier} | R Documentation |
The simulated 10% and 5% points of the distribution of DMCE.
Description
The data used in here, obtained by using Monte-Carlo simulation method.
Usage
data("DMCE")Details
A simulation study is carried out to find the percentile (cut-off) point of DMCE by using Monte-
Carlo methods. Fifteen different sample sizes are used, namely n = 10, . . . , 150. For each
sample size n, a set of random circular errors is generated from the von Mises distribution with
mean direction 0 and various values of concentration parameter k = 1, 2, . . . , 100. Samples
of von Mises distribution VM(\pi/4, 10) with corresponding size n are generated to represent the
values of X variable. The parameters of model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi)
(i=1,2,...,n) are fixed at \alpha=0 and \beta=1. Observed values
of the response variable y are calculated based on model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi)
(i=1,2,...,n) and subsequently the fitted values Y
are obtained. We then compute the value of the MCE statistic for full data set. Sequentially, we
exclude the ith observation from the generated sample, where i = 1, . . . , n. We refit the reduced
data using model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi) (i=1,2,...,n)
and then calculate the values of MCe. Then, we obtain the value of DMCE.
The process is carried out 2000 times for each combination of sample size n and concentration
parameter k.
References
A. H. Abuzaid, A. G. Hussin & I. B. Mohamed (2013) Detecting of outliers in simple circular regression models using the mean circular error statistics.