Hypothesis testing on zero-inflated continuous data I would greatly appreciate your advice on the following problem:
I've got a large continuous dataset with lots of zeros (~95%) and I need to find the best way to test whether certain subsets of it are "interesting", ie don't seem to be drawn from the same distribution as the rest. Zero inflation comes from the fact that each data point is based a count measurement with both true and sampling zeros, but the result is continuous as it takes into account some other parameters weighted by the count (and so if the count is zero, the result is also zero).
What would be the best way to do this? I have a feeling that Wilcoxon and even brute-force permutation tests are inadequate as they get skewed by these zeros. Focussing on non-zero measurements also removes true zeros that are extremely important. Zero-inflated models for count data are well-developed, but unsuitable for my case. 
I considered fitting a Tweedie distribution to the data and then fitting a glm on response=f(subset_label). Theoretically, this seems feasible, but I'm wondering whether (a) this is overkill and (b) would still implicitly assume that all zeros are sample zeros, ie would be biased in the same way (at best) as a permutation?
Intuitively, it sounds like have some kind of hierarchical design that combines a binomial statistic based on the proportion of zeros and, say, a Wilcoxon statistic computed on non-zero values (or, better still, non-zero values supplemented with a fraction of zeros based on some prior). Sounds like a Bayesian network... 
Hopefully I'm not the first one having this problem, so would be very grateful if you could point me to suitable existing techniques...
Many thanks!
 A: @msp, I think you are looking at a two stage model in that attachment (I did not have time to read it), but zero inflated continuous data is the type I work with a lot. To fit a parametric model to this data (to allow hypothesis tests) you can fit a two stage but then you have two models (Y is the target and X are covariates): P(Y=0 |X) and P(Y|X;Y>0). You have to use simulation to "bring" these together. Gelmans book (and the arm package in R) shows this process for this exact model (using logistic regression and ordinary linear regression with a log link). 
The other option I have seen and like better is to fit a zero inflated gamma regression, which is the same as above (but gamma as the error instead of guassian) and you can bring them together for hypothesis tests on P(Y|X). I dont know how to do this in R, but you can in SAS NLMIXED. See this post, it works well.  
A: A similar approach to the Fletcher paper is used in marketing testing, where we can arbitrarily separate the effects of interventions (such as advertising) into (a) a change in the number buying the brand (i.e. proportion of zeroes) and (b) a change in the frequency of buying the band (sales given sales occur at all).  This is a solid approach and conceptually meaningful in the marketing context and in the ecological context Fletcher discusses. In fact, this can be extended to (c) a change in the size of each purchase.
A: You could treat the exact number of zeros unknown, but constrained between 0 and the observed number of zeros. This can surely be handled using a Bayesian formulation of the model. Maybe a multiple imputation method can also be tweaked to appropriately vary the weights (between 0 and 1) of the zero observations…
