Newsgroups: alt.paranet.ufo
From: kymhorsell@gmail.com
Subject: ufos and unexplained amnesia

[uploaded 43 times; last 01/11/2024]

EXECUTIVE SUMMARY:
- We examine an interesting category of FBI missing people --
  "unidentified but living". Most unidentified missing people are just
  bodies or body parts discovered various places.  But the FBI also
  has a category of people found alive but unable to say who they are.
  I'll use the mental model "amnesia" here.
- While the category shows strong statistical links with the "usual
  suspects" using previously-described s/w -- variables like
  Antarctic temperatures, phytoplankton blooming in the Southern
  Ocean, positions of Saturn, mid-Atlantic earthquakes, etc. -- we ramp
  up the pressure here to ensure the links are validated.  I.e. 1/3
  of the data is used to estimate the relevant link and then the other
  2/3 of the data is checked to ensure it has the same link.
- On top of that we removing all seasonal and annual trends and cycles
  from the data to leave what can be thought of as "noise" or the
  data's "fingerprint".  If the fingerprint of X is also found in Y it
  suggests the link between the 2 is rather stronger than just an
  "association" and is possibly of the "causes" type.
- We find certain UFO types seem to be causally connected with amnesia
  cases found by the FBI. It also seems some links are -ve meaning
  some UFO types may be "friendly".
- But overall we must remember just as UFO's are a complex combination
  of phenomena -- "not just the one thing" -- even single
  categories are likely to have a lot of complexity.  In a human
  community some people do some things and some do the opposite.
  We can't judge individuals just from the average for the whole community.


I was interested to see some comments from Luis Elizondo about cases
reported to the secret UFO unit he led concerning military people that
had been kidnapped by UFOs.

We've taken a look at the civilian data from the FBI that suggests
there is some connection between UFO activity and certain types of FBI
missing persons cases -- of which there are 10s of 1000s every year.

But here I'd like to look at one very special category the FBI is
interested in. Persons found wandering around and unable to remember
who they are.

While we can assume there are sometimes reasons people *want* to
forget or pretend to forget who they are and what they have done,
we'll just let the stats sort out whether there is some connection
between these cases -- usually 20-30 per month since at least 2015 --
and UFO activity across the US.

And we'll tighten the elastic bands on our programs to try to make it
absolutely sure there is a connection far beyond the certainty usually
provided by simple statistical tests.

But how do we ensure some "X" really is the cause of some "Y"?  This
is a hot topic in data science and there are a growing set of methods
to determine causation.

I will use a grab bag of methods here, starting with ideas first
introduced in the 1960s Surgeon General's report linking cancer and
smoking. In that report it was argued that 3 things must be done to
"scientifically prove" X causes Y. First, if X changes at time T then
sometime later Y must change. Second, if Y changes at time T then X
must have changed previously.  And third, in states or age-groups or
other ways of categorizing the data different levels of X must be
associated with commensurate values of Y.

Part of this is handled by the idea of "cross validation" in
statistical model building. The idea is to take part of the data to
determine relevant trends, and then see if that same trend explains
equally well parts of the data that were not involved in estimating
the trend.

My s/w does an even more extreme thing. It divides time series into 3
equal parts. It uses the middle part to estimate trends and then
checks that the same trend is seen in the first and third parts --
i.e. it makes sure the model "back casts" and "fore casts" results not
seen when estimating the model.

To make it even more solid we wont just use the original data. We will
manipulate it to extract JUST THE NOISE.

The idea here is that the noise in a data series is like a
"fingerprint".  In humans and animals most of the cell's DNA is
"noise" and even identical twins have different so-called "junk DNA".
If the same junk DNA is found at a crime scene it's very certain it's a
given person and not a relative or even a twin.

So if X causes Y then we would expect the noise part of X should also
end up being part of the noise in the Y data.
(I came across a similar idea when working on a production line as a
teenager. Working a machine with unfamiliar controls and very little
instruction from the foreman I found I could get a handle on what knob
caused what action of the machine by flicking the knobs and levels to
their max and min extent. Pushing a lever to the extreme top quickly
tells you what is connected with it; then throwing the lever to the
extreme bottom confirms the connection if the same thing moves to its
opposite extreme. Don't worry. Companies are insured for this kind of thing :).

If some X and Y passes all tests we might argue it is highly
suggestive of a causal link.

The data for the FBI's "unidentified and living" category that
features people that generally can't remember who they are (the rest
of the unidentified are dead bodies or body parts -- and we will look
at this part of the data sometime when I'm not eating) from 2015-2021
which are available online from the FBI looks like:

year.mo         Num of new unident living/amnesia cases
2015.04         15
2015.12         25
2015.21         15
2015.29         25
2015.38         15
2015.46         35
2015.54         15
2015.62         15
2015.71         25
2015.79         15
2015.88         15
2015.96         15
2016.04         11
2016.12         21
2016.21         14
2016.29         11
2016.38         12
2016.46         15
2016.54         20
2016.62         9
2016.71         11
2016.79         17
2016.88         16
2016.96         9
2017.04         10
2017.12         19
2017.21         10
2017.29         17
2017.38         12
2017.46         19
2017.54         26
2017.62         22
2017.71         19
2017.79         22
2017.88         15
2017.96         16
2018.04         9
2018.12         16
2018.21         18
2018.29         13
2018.38         20
2018.46         21
2018.54         15
2018.62         14
2018.71         11
2018.79         25
2018.88         23
2018.96         14
2019.04         12
2019.12         18
2019.21         17
2019.29         29
2019.38         28
2019.46         17
2019.54         14
2019.62         23
2019.71         20
2019.79         10
2019.88         21
2019.96         17
2020.04         25
2020.12         12
2020.21         27
2020.29         11
2020.38         23
2020.46         15
2020.54         10
2020.62         26
2020.71         31
2020.79         40
2020.88         24
2020.96         14
2021.04         27
2021.12         19
2021.21         29
2021.29         17
2021.38         22
2021.46         27
2021.54         28
2021.62         38
2021.71         36
2021.79         33
2021.88         13
2021.96         22

If we pass this through our old s/w that looked for any dataset that
seems similar to high statistical certainty it spits out dozens of
things some of which seem expected -- earthquakes in particular
locations, tornadoes, hurricanes, etc -- and a whole lot of things
that seem weird -- temperatures in very remote locations esp the
Antarctic; UFO activity in certain states; positions of certain
planets; bigfoot sightings; reports of poltergeists in the UK.

Some of this weird stuff we've seen before in other categories of
missing people. And I wouldn't be letting the cat out of the bag too
much to say a large number of missing person datasets from around the
world -- esp for unsolved/long-time cases -- show very much the same
robust statistical links.

But given some kind of UFO activity does show up from this s/w, we can
put the question about which kinds of UFO activity passes our bevvy of
new, much stronger, tests incl ability to accurately extrapolate to
years before and after the part used to estimate the models.

And we find the following table:

Fit             UFO type             Model
0.158696        Disk                 0.00105588*x + 0.407719
0.159203        Cigar                1.39737e-05*x + 0.422389
0.159922        Rectangle            -0.00179053*x + 0.435333
0.16084         Triangle             -0.000486553*x + 0.436003
0.162856        Chevron              0.0103198*x + 0.379747
0.164306        Cylinder             0.0120401*x + 0.362264
0.164455        Diamond              0.00605385*x + 0.382647
0.166968        silver               0.00570325*x + 0.401347
0.167437        Cone                 0.0285495*x + 0.381177
0.16776         Cross                -0.00266536*x + 0.436264
0.171013        yellow               0.0075008*x + 0.34129
0.174811        Flash                0.0106606*x + 0.329221
0.175515        Circle               0.001022*x + 0.377576
0.176453        Sphere               0.00286849*x + 0.355092
0.176833        gold                 0.0302667*x + 0.323031
0.181182        black                0.0123185*x + 0.331007
0.18146         Other                0.00353659*x + 0.336592
0.184754        Changing             0.0141074*x + 0.288187
0.191114        blue                 0.00625052*x + 0.324576
0.192307        Formation            0.00354779*x + 0.38318
0.19277         Egg                  0.0305156*x + 0.336981
0.194887        Teardrop             0.0291754*x + 0.335123
0.195857        Oval                 0.00831132*x + 0.317447
0.198673        red                  0.00198329*x + 0.293622
0.211557        grey                 0.109001*x + 0.254896
0.214707        Unknown              0.00728548*x + 0.259813
0.225868        green                0.00708822*x + 0.278383
0.287855        Fireball             0.00883991*x + 0.268689
0.312456        Light                0.00268957*x + 0.227228

The "fit" shows the maximum error as a faction of the maximum value
for the amnesia data. I.e. the first and best model gave no more than
a 16% error estimating month by month using the UFO type and the
relevant model. This error not only applied to the middle 1/3 of the
data used to estimate the model shown, but it also worked in the other
67% of the data the model had not seen.

The UFO type is the color or shape from the relevant NUFORC database entry.
(I again thank the NUFORC for transparently providing their data online).

And the model shown links the number of monthly cases of the UFO type
(x) with the predicted number of amnesia cases found by the FBI for
that month (y).  (And remember, I've scaled that amnesia data to make
the maximum monthly value 1.0).

While it may be difficult to compare the relative strength of the
links (because the monthly number of each UFO type wildly varies with
"Light" types almost 1/2 the total and some of the colors only seen
every couple of months) we can immediately see some interesting things.

For one, a minority of the models has a -ve sign. For "Triangle" UFO's
it seems the more than are seen the fewer amnesia cases are detected.

Are these ones "on out side"? :)

I'm sure the answer is more complex than that. As ufologists and
lately the Pentagon warns -- UFO's are not just the one thing.  And
even one category of UFO also seems not to be just the one thing.
Oftentimes I see a certain category of object linked both +ve and -ve
with various activities. After all we would expect disappearance of
artwork to be both +ve and -ve linked with different categories of
human beings. While some might be art thieves a lot of them aren't.
So they tell me.

But given the prediction error (fit) is less than 20% of the maximum
number of amnesia cases and the matching was based on just the "noise"
in the data (i.e. after all seasonal factors and annual trends were
subtracted out) we can be fairly sure even data coming in after 2021
will very likely be predicted by the same models.

Which seems to be some evidence that UFO activity is involved in
"somehow" creating a small number of cases most months where people
are discovered and exhibit what we might call symptoms of being "neuralized".

We can't definitely say that UFO's are performing the neuralizing but
we can see whatever noise is present in the month to month changes in
UFO activity of each type is robustly mirrored in the noise of the
month to month amnesia cases. Hard to explain how that happened
without actually "doing" something.

And, as I outlined at the start, it seems there is some kind of
"official evidence" that "UFO's have kidnapped military personnel".
Interesting to know -- if that might be revealed in the supposedly
open hearings in Congress that are to happen today -- if any of the
military personnel have been returned with amnesia and Antarctic snow
on their boots.

--
"Nothing in life is to be feared, it is only to be understood.
Now is the time to understand more, so that we may fear less."
- Marie Curie

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