Description Usage Arguments Details Value Author(s) References Examples

It compute the JLMn-statistic, from a bivariate sample of continuous random variables X and Y.

1 | ```
JLMn(x, y)
``` |

`x, y` |
numeric vectors of data values. x and y must have the same length. |

See subsection 3.3-Main reference. For sample sizes less than 20, the correction introduced in subsection 3.2 from main reference, with c = 0.4 was avoided.

The value of the JLMn-statistic.

J. E. Garcia, V. A. Gonzalez-Lopez

J. E. Garcia, V. A. Gonzalez-Lopez, Independence tests for continuous random variables based on the longest increasing subsequence, Journal of Multivariate Analysis (2014), http://dx.doi.org/10.1016/j.jmva.2014.02.010

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# mixture of two bivariate normal, one with correlation 0.9 and
# the other with correlation -0.9
#
N <-100
ro<- 0.90
Z1<-rnorm(N)
Z2<-rnorm(N)
X2<-X1<-Z1
I<-(1:floor(N*0.5))
I2<-((floor(N*0.5)+1):N)
X1[I]<-Z1[I]
X2[I]<-(Z1[I]*ro+Z2[I]*sqrt(1-ro*ro))
X1[I2]<-Z1[I2]
X2[I2]<-(Z1[I2]*(-ro)+Z2[I2]*sqrt(1-ro*ro))
plot(X1,X2)
#calculate the statistic
a<-JLMn(X1,X2)
a
``` |

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