  The problem of simultaneous estimation of order restricted location
parameters $\theta_1$ and $\theta_2$ ($-\infty<\theta_1\leq \theta_2<\infty$)
of a bivariate location symmetric distribution, under a general loss function,
is being considered. In the literature, many authors have studied this problem
for specific probability models and specific loss functions. In this paper, we
unify these results by considering a general bivariate symmetric model and a
quite general loss function. We use the Stein and the Kubokawa (or IERD)
techniques to derive improved estimators over any location equivariant
estimator under a general loss function. We see that the improved Stein type
estimator is robust with respect to the choice of a bivariate symmetric
distribution and the loss function, as it only requires the loss function to
satisfy some generic conditions. A simulation study is carried out to validate
the findings of the paper. A real-life data analysis is also provided.
