based on the inverse participation ratio (IPR) are also found to be good candidates for estimating the extent of entanglement  [@ViolaBrown; @sharmila2]. The IPR corresponding to a bipartite system in the basis of the rotated quadrature operators is defined as $$\eta_{\textsc{ab}}({\theta_{\textsc{a}}},{\theta_{\textsc{b}}}) = \int_{-\infty}^{\infty} \!\rmd X_{{\theta_{\textsc{a}}}} \int_{-\infty}^{\infty} \!\rmd X_{{\theta_{\textsc{b}}}} [w(X_{{\theta_{\textsc{a}}}},{\theta_{\textsc{a}}};X_{{\theta_{\textsc{b}}}},{\theta_{\textsc{b}}})]^{2}.
\label{eqn:IPRab}$$ The IPR for each subsystem is given by $$\eta_{i}(\theta_{i}) = \int_{-\infty}^{\infty} \!\rmd X_{\theta_{i}} [w_{i}(X_{\theta_{i}},\theta_{i})]^{2} \;\; 
(i= \textsc{A,B}).
\label{eqn:IPRi}$$ The entanglement indicator in this case is given by $${\varepsilon_{\small\textsc{ipr}}}({\theta_{\textsc{a}}},{\theta_{\textsc{b}}})=1+\eta_{\textsc{ab}}({\theta_{\textsc{a}}},{\theta_{\textsc{b}}})-\eta_{\textsc{a}}({\theta_{\textsc{a}}})-\eta_{\textsc{b}}({\theta_{\textsc{b}}}). 
\label{eqn:epsIPR}$$

Apart from these, we have examined two other correlators which are familiar in the context of classical tomograms. The first of these is the Pearson correlation coefficient [@SmithMIcorr] between two random variables $X$ and $Y$, given by $$\nonumber 
\textsc{PCC}(X,Y)
 = \frac{\mathrm{Cov}(X,Y)}{\sigma_{\textsc{x}} \sigma_{\textsc{y}}}.
\label{eqn:PCC}$$ Here $\sigma_{\textsc{x}}, \sigma_{\textsc{y}}$ are the standard deviations of $X$ and $Y$ respectively, and $\mathrm{Cov}(X,Y)$ is their covariance. Of direct relevance to us is $\textsc{PCC}(X_{{\theta_{\textsc{a}}}},X_{{\theta_{\textsc{b}}}})$ calculated for fixed values of ${\theta_{\textsc{a}}}$ and ${\theta_{\textsc{b}}}$. Since the quantifier of entanglement between two subsystems must be non-negative, a simple definition of the entanglement indicator in this case would be $${\varepsilon_{\small\textsc{pcc}}}({\theta_{\textsc{a}}},{\theta_{\textsc{b}}})=
\vert \textsc{PCC}(X_{{\theta_{\textsc{a}}}},X_{{\theta_{\textsc{b}}}})\vert.
\label{eqn:epsPCC}$$ This indicator captures the effect of linear correlations. Our motivation for assessing this indicator arises from the fact that, 