# Regressing $x$ on $y$ for Count Data

Suppose I have data $x$ and $y$, where $x$ is a count and $y$ is continuous. I would like to predict $x$ from $y$.

Specifically, for my research question, $X$ can be viewed as being measured without error (it is fixed by design), whereas $Y$ is random.

Below is my data

$x$:  [1] 1 2 3 4 5 6 7 8 9 10

$y$:  [1] 1.0000 1.8002 2.4383 2.9353 3.3641 3.6847 3.9578 4.1610 4.3139 4.4667


The above data come from a simulation that I have developed to generate asymptotic "accumulation" curves. Basically, my simulation randomly samples without replacement from a pool of distinct character labels and computes the mean across all individuals (represented by the $x$ data). For the above data there are 5 character labels. I want to see if I can recover all 5 distinct labels. Based on the above data, only 4.4667 labels have been recovered on average.

What I am looking for is a regression technique that I can apply to the kind of data that I have supplied.

Specifically, I would use the proposed regression method to answer a question such as "What is the value of $x$ for a corresponding $y$-value of $y$ = 5?" That is, in the context of my data, what $x$ is needed to observe exactly $y$ = 5 labels?

I am unaware of existing appropriate alternatives that could work in this setting besides inverse regression.

• What are "nonlinear data"? Why do you say they "don't fit any parametric model"? Is $n$ known/fixed? Could each $x$ be considered the sum of $n$ independent dichotomous observations? Explaining what $x$ & $y$ are would probably help make your question a lot clearer. – Scortchi - Reinstate Monica Oct 4 '17 at 15:52
• You really need to tell us what x and y represents – kjetil b halvorsen Oct 4 '17 at 16:36
• With regard to the latest edit: is $x$ fixed by design? What's the sampling scheme? If you don't explain anything at all about what the data represent and how they were obtained how can a suitable regression model be suggested? – Scortchi - Reinstate Monica Oct 4 '17 at 20:49
• @whuber The "controversy" I speak of refers to predicting $x$ from $y$ using "classical" vs. inverse regression estimators. The literature by Brandon Greenwell is an excellent source here. – compbiostats Oct 4 '17 at 21:31
• Thank you. Interestingly, the top hit in a Google search turned up Greenwell's investr package for R, which offers many different ways to carry out inverse regression! He refers to this as "classical and well-known," so it's hard to see where the controversy might be. – whuber Oct 4 '17 at 21:34