Summary: Let \((X_i, Y_i)\), \(i=1, 2,\dots, n\), be \(n\) independent and identically distributed random variables from some continuous bivariate distribution. If \(X_{(r)}\) denotes the \(r\)th ordered \(X\)-variate then the \(Y\)-variate, \(Y_{[r]}\), paired with \(X_{(r)}\) is called the concomitant of the \(r\)th order statistic. We obtain new general results on stochastic comparisons and dependence among concomitants of order statistics under different types of dependence between the parent random variables \(X\) and \(Y\). The results obtained apply to any distribution with monotone dependence between \(X\) and \(Y\).
In particular, when \(X\) and \(Y\) are likelihood ratio dependent, it is shown that the successive concomitants of order statistics are increasing according to likelihood ratio ordering and they are \(TP_2\) dependent in pairs. If we assume that the conditional hazard rate of \(Y\) given \(X=x\) is decreasing in \(x\), then the concomitants are increasing according to hazard rate ordering and are dependent according to the right corner set increasing property. Finally, it is proved that if \(Y\) is stochastically increasing in \(X\), then the concomitants of order statistics are stochastically increasing and are associated.
Analogous results are obtained when the variables \(X\) and \(Y\) are negatively dependent. We also prove that if the hazard rate of the conditional distribution of \(Y\) given \(X= x\) is decreasing in \(x\) and \(y\), then the concomitants have DFR (decreasing failure rate) distributions and are ordered according to dispersive ordering.