Weighted normal errors regression with censoring I have some data which I would model via standard multiple regression except:

*

*There is censoring (left-censored, fixed but varying censoring points which are known)

*The errors are assumed independent normal but of non-constant variance.  Weights are available.

If it was constant variance, I would use the Tobit model and survreg() function in R.  Does anyone know of the/an approach when the variance is not constant (but weights for variances are available)?
 A: There must be some weight arguments to the survreg function? Anyhow, this can be solved by setting up a likelihood function from first principles.
You have a normal model (with independent observations) and known weight, the optimal weights are the inverse variances, so write the weight as $w_i$ taken to be the inverse of known variances. Then we can write the density as
$$
    f(x:\mu) = \frac{\sqrt{w_i}}{\sqrt{2\pi}} e^{-\frac12 w_i (x_i-\mu)^2}
$$
Assume the censoring points are at $t_i$, the first $r$ obs are fully observed and observations $r+1 \dotsc n$ censored.  Then the likelihood becomes
$$
   L(\mu) = \prod_1^r f(x_i; \mu) \prod_{r+1}^n \Phi(\sqrt{w_i}(t_i-\mu))
$$
where $\Phi$ denotes the standard normal cdf.  The loglikelihood becomes 
$$
  l(\mu) = -\frac12\sum_1^r w_i (x_i-\mu)^2 + \sum_{r+1}^n \log \Phi(\sqrt{w_i}(t_i-\mu))
$$
where we have left out some terms not influencing the shape of the loglikelihood function.  Now this function can be sent to a numerical optimization routine to find the maximum likelihood estimator of $\mu$. 
