DMCE: The simulated 10% and 5% points of the distribution of DMCE.

In CircOutlier: Detection of Outliers in Circular-Circular Regression

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(π/4, 10) with corresponding size n are generated to represent the values of X variable. The parameters of model y_i=α+β x_i+ε_i (mod 2π) (i=1,2,...,n) are fixed at α=0 and β=1. Observed values of the response variable y are calculated based on model y_i=α+β x_i+ε_i (mod 2π) (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=α+β x_i+ε_i (mod 2π) (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.

CircOutlier documentation built on May 2, 2019, 6:04 a.m.