I am trying to derive (one-sided) tolerance intervals related to the Deming regression model: $$ x_i=x^*_i + \epsilon_i$$ $$ y_i = (\alpha+\beta x^*_i) + \epsilon'_i$$ where the $x^*_i$'s are nonrandom fixed numbers, $\epsilon_i \sim {\cal N}(0,\sigma_x^2)$, $\epsilon'_i \sim {\cal N}(0,\sigma_y^2)$, and all variables $\epsilon_i, \epsilon'_i$ are mutually independent. The ratio $\sigma_x^2/\sigma_y^2$ is assumed to be known. Put $\tau^2=\sigma_x^2+\sigma_y^2$.
Given a new ``true $x$'' value $x^*_\text{new}$ I'm looking for an upper tolerance bound of the difference $$y_\text{new} - x_\text{new} \sim {\cal N}(\alpha+(\beta-1)x^*_\text{new}, \tau^2).$$ Actually this upper tolerance bound is nothing but an upper $100(1-\alpha)\%$-confidence bound about the $100p\%$-quantile of $y_\text{new} - x_\text{new}$. As for classical Gaussian models I'm looking for a tolerance bound having form $$B(x,y)=(\hat{y^*}_\text{new} - x^*_\text{new}) + K s$$ with $\hat{y^*}_\text{new} = \hat\alpha + \hat\beta x^*_\text{new}$ and $s^2= \hat\tau^2$. Denoting by $F_\text{new}$ the cumulative distribution function of the difference $y_\text{new} - x_\text{new}$, the tolerance factor $K$ must be derived in order that $$\Pr\left( F_\text{new}(B(x,y)) > p \right) = 1-\alpha$$ for some given $p\in]0,1[$ (the tolerance level) and confidence level $1-\alpha$ (of course here $\alpha$ is not the intercept parameter $\alpha$ of the model!)
Estimates of $\alpha$, $\beta$ and $\tau^2$ as well as their asymptotic variance matrix $V$ and an estimate $\hat V$ of this asymptotic variance matrix are available in some textbooks and/or research papers (see for instance Fuller's book or Iles & Gillard's report 1 and report2). In particular $\hat\tau^2$ is asymptotically independent of $(\hat\alpha,\hat\beta)$.
Now I'm trying to derive the tolerance bound by miming the way used for deriving the tolerance bound in a classical regression model. It is based on the following lemma.
Lemma Let $W \sim {\cal N}(0, \sigma^2)$ independent of $Q \sim \frac{\chi^2_{d}}{d}$. Then the number $K>0$ satisfying $$\Pr\left( \Phi(W+K\sqrt{Q}) > p \right) =1-\alpha$$ is given by a known formula $K=K(\sigma,d,p,\alpha)$ (see Krishnamoorthy & Mathew's book).
Now put $X_\text{new}=(1,x_\text{new})'$. One has the three asymptotically independent random variables $$ \frac{(y_\text{new} - x_\text{new} ) - (\alpha+(\beta-1)x^*_\text{new})}{\sqrt{\tau^2}} \sim {\cal N}(0,1),$$ $$ \frac{(\hat{y^*}_\text{new} - x^*_\text{new} ) - (\alpha+(\beta-1)x^*_\text{new})}{\sqrt{\tau^2}} \sim {\cal N}\left(0,\frac{X_\text{new}'VX_\text{new}}{\tau^2}\right),$$ $$\frac{s^2}{\tau^2} \sim \frac{\chi^2_{n-2}}{n-2}$$
The definition of $K$ becomes $$\Pr\left( \Phi(W+K\sqrt{Q}) > p \right) =1-\alpha$$ with $W \sim {\cal N}\left(0,\frac{X_\text{new}'VX_\text{new}}{\tau^2}\right)$ independent of $Q \sim \frac{\chi^2_{n-2}}{n-2}$.
Applying the lemma we get $K$ depending on the unknown values of $\tau$ and $V$. Substituting with the estimates $\hat\tau$ and $\hat V$ gives an approximate tolerance factor $\hat K$.
Well, I have done it and I have implemented everything in R. But assessing the performance of my tolerance bound with simulation shows that even with large sample sizes it does not work, except for the case $\beta=1$. I'm pretty sure of my formulas for the estimates and the variance matrices since I have also implemented confidence intervals and prediction intervals and they work well.
EDIT: In fact that works !! There was a silly error in the R code I used for simulations. Should I delete this post ? I think no because the method could interest others.
