## Friday, June 20, 2014

### Final comments on the hurricane study

I sent a letter to the editors of the Proceedings of the National Academy of Sciences summarizing the point I made in my post of June 12.  They declined to publish it (I hope that's because they had already accepted one or more letters making the same point), but I have posted it on my UConn web page.

It occurred to me that the hurricane study omitted the two hurricanes that caused the largest number of deaths (Audrey in 1957 and Katrina in 2005) because the models couldn't fit them--basically, they had too many deaths to be plausible under the distributions that they used.  But both hurricanes had female names, so they should be counted as some kind of favorable evidence for their hypothesis.  What sort of models could accommodate all of the hurricanes?  There are two reasonable approaches:

1.  Sophisticated:  The Cox proportional hazards model is widely used for duration data--time until some event.  Count data is like duration data in the sense that there is only one possible direction of change--just as a person who's turned 90 can't go back and die at 89, a hurricane that's killed 90 can't go back and wind up killing only 89.  So the Cox model can reasonably be applied to count data, although I don't think I've ever seen that done.  The model is useful because it makes minimal assumptions about the distribution--it essentially just tries to predict which cases will rank higher than others.  If you add Katrina and Audrey to the data set (I scored them both as highly feminine names), the estimated effect of feminine name is .031 with a standard error of .032, which is nowhere near statistical significance.

2.  Simple:  take the logarithm of (deaths+0.25).  You need to add the small constant because many hurricanes caused zero deaths and the logarithm of zero is undefined.  The exact value doesn't matter much.  Then do an ordinary linear regression with the log of (deaths+.25) as dependent variable and log of damage and hurricane name as the predictors.  The estimated effect of feminine name is .024 with a standard error of .043, again nowhere near significance.  The residuals from the model are approximately normally distributed, meaning that the estimate and se are trustworthy.

The interaction between name and damage is not statistically significant either way.