It's not in this PDF. Unless you meant something else?

http://anonymous.coward.free.fr/...cda/indirect-cda.pdf

Minimizing squared error has certain very desirable properties from the point of view of
statistical inference; however, if the method is not robust to error, statistical inference is
unimportant.

In spirit, this is similar to the method of maximum likelihood; in this case, CdA is chosen to
maximize the “likelihood” (loosely defined) of observing elevation profiles with zero net
elevation gain from lap to lap. Another approach that may be familiar is Laplace transforms.
Like Laplace transforms, this is an integral transform that converts a sequence of data
collected in time domain into a function of a different variable: in this case, distance and
elevation as a function of CdA. Of course, a transform only makes sense if the alternative
either simplifies the analysis or provides some new insight on the relationships in the original
form. That insight is the elevation profile implied by the data. In addition, we're adding laps
so we can create a new constraint and exploit a natural “periodicity” in the data. That kind of
periodicity doesn't exist in other approaches. Later we'll see other ways to create the proper
periodic contrasts in different settings and that will give us hints on how to construct good
test protocols.

As mentioned earlier, another way of thinking of this is as a generalization of a coast down
test. In a typical coast down, you coast from a known speed down to another known speed
on a surface of known slope. In that case, you're applying a known power: zero. In this case,
you're doing a “coast down” with known non-zero power, and using the recorded speed to
tell you how quickly you're decelerating. See H.W. Schreuder (op. cit.) for a discussion of
high precision coast downs.

Note that the flatter the course the greater potential effect of unmeasured wind since wind
will be larger relative to the true elevation.

You want the errors to be small relative to the modeled parts. In the usual approach you
tightly control speed, acceleration, and the slope and you choose windless days. In this
approach you don't have to control the speed and acceleration since they're measured well.
However, you want a good spread of speeds and a reasonable amount of change in elevation
to help “isolate” wind effects.

That is, if you know the true elevation profile it gives you a good way to assess how much
the estimate was affected by unmeasured wind. This turns out to be useful: the usual
approach is to wait for a wind-free day, to test on a flat (or constant slope) road, to hold
speed constant (or, at least, to minimize changes in speed) but there is no simple way to tell if
the measurements were tainted by wind, or changes in speed, or a small degree of slope, or a
slight change in position.

If you ride laps, you can “overlay” them to see how similar the VE profiles are for each of the
laps. If they're very different, you know it was too windy, or you didn't hold your position, or
something else happened to the measurements, and you can see if the difference was transient
or perduring.

Here's another way to think of it: we're trying to raise the “signal-to-noise” ratio. The
classical approach to field testing tries to increase this ratio by decreasing the noise.
Decreasing noise is always a good thing but another approach is to decrease noise and to
increase the signal. This approach models accelerations and “sequences” the data in order to
increase the signal, then re-casts the model in a way that lets us measure deviations from fit.

Let's review: using only power and speed, we can show that the calculated profiles are
relatively inelastic to mass. Most people doing field testing would at least make an attempt to
measure air density but I didn't so the best we can do here is to produce an estimate for CdA
that depends on Crr and air density.

For given Crr, increasing air density implies decreasing CdA, and a 1% change in air density
implies around a 1.5% change in CdA.

For given air density, increasing Crr implies decreasing CdA, and a 1% change in Crr implies
around a 0.3% change in CdA.

This may make it sound like Crr is less important than air density, but air density is easy to
measure and it changes relatively slowly while Crr is hard to measure well, road surfaces can
change quickly, and changing road surfaces can change Crr by much more than 1%. The
bottom line is that although CdA is relatively less sensitive to changes in Crr than to change
in air density, the magnitude of changes in Crr can be large so the overall effect is also large.
Conversely, if you're off on air density by a little bit, it won't affect CdA that much. Bottom
line, you should probably do your best to record air temperature and barometric pressure, but
don't sweat too much about air density changing over the course of your runs.

Here's an important observation: for these data, the total elevation change doesn't appear to
be that sensitive to changes in Crr. That's so for these data but it will turn out that this is not
always the case; in fact, we'll exploit this difference later.