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Entropy2016,18, 375
of its sine) takes the former intoaccount, the latterhasnotbeenexploitedyet. Todoso,weneedto
estimateE(Im(μ−1Z))2.
Consider Vn = 1n∑ n
k=1Y 2
k that is under the hypothesis that the corresponding ζ is the
unique circular population mean has expectation σ2 = Var(Yk) = E(Im(ζ−1Z))2. Now,
1−Vn= 1n∑nk=1(1−Y2k) is themeanofn independent randomvariables takingvalues in [0,1]and
having expectation 1−σ2. By another application of Equation (6), we obtain P(σ2 ≥ Vn+ t) =
P(1−Vn ≥ 1−σ2+ t) ≤ α4 for t = t(α4,1−σ2,0,1), the latter existing if α4 > (1−σ2)n.
Since 1−σ2+ t(α4,1−σ2,0,1) increases with 1−σ2, there is a minimal σ2 for which 1−Vn ≥
1−σ2+ t(α4,1−σ2,0,1)holdsandbecomesanequality;wedenote itby σ̂2=Vn+ t(α4,1− σ̂2,0,1).
Inserting intoEquation(6), itbyconstructionfulfills
α
4 = [( 1− σ̂2
1−Vn )1−Vn( σ̂2
Vn )Vn]n
. (9)
It is easy to see that the right-handsidedependscontinuouslyonand is strictlydecreasing in
σ̂2 ∈ [Vn,1] (seeAppendixA), thereby traversing the interval [0,1] so thatone canagain solve the
equationnumerically.Wethenmay,withanerrorprobabilityofatmost α4,use σ̂ 2 asanupperbound
forσ2.Note that σ̂2>Vn exists if α4 > (1− σ̂2)n.The latter is fulfilledforanyVn<1sinceEquation(9)
isequivalent to
α
4 = ( 1− σ̂2)n[( 1
1−Vn )
︸ ︷︷ ︸
>1 ( 1− σ̂2
1−Vn )−Vn
︸ ︷︷ ︸
>1 ( σ̂2
Vn )Vn
︸ ︷︷ ︸
>1 ]n
.
ForVn=1, let σ̂2=1be the trivialbound.
With such anupper bound on its variance, we now can get a better estimate forP(Y¯n > t).
Indeed,onemayuseanother inequalitybyHoeffding[11] (Theorem3): themeanW¯n= 1n∑ n
k=1Wkof
asequenceW1, . . . ,Wnof independentrandomvariables takingvalues in (−∞,1], eachhavingzero
expectationaswellasvarianceρ2 fulfills
P ( W¯n≥w )≤[(1+ w
ρ2 )−ρ2−w(
1−w )w−1] n1+ρ2
, (10)
≤ exp(−nt[(1+ ρ2t ) ln(1+ tρ2)−1]). (11)
for any w ∈ (0,1). Again, an elementary calculation (analogous to Lemma A1) shows that the
right-hand side of Equation (10) is strictly decreasing in w, continuously ranging between 1
and(
ρ2
1+ρ2 )n aswvaries in (0,1), so that thereexistsauniquew=w(γ,ρ2) forwhichtheright-handside
equalsγ,providedγ∈ (( ρ2
1+ρ2 )n,1).Moreover, theright-handside increaseswithρ2 (asexpected),
so thatw(γ,ρ2) is increasing inρ2, too (cf.AppendixA).
Therefore,under thehypothesis that thecorrespondingζ is theuniquecircularpopulationmean,
P (|Y¯n| ≥ w(α4,σ2)) ≤ 2α4 = α2. Now, sinceP(w(α4,σ2) ≥ w(α4, σ̂2)) = P(σ2 ≥ σ̂2) ≤ α4, setting
sV=w(α4, σ̂ 2)wegetP (|Y¯n|≥ sV)≤ 34α.Note that ρ21+ρ2 increaseswithρ2, so incase s0 exists, σ̂2≤1
implies α4 >2 −n≥ (
σ̂2
1+σ̂2 )n
, i.e., theexistenceof sV.
FollowingtheconstructionforCH fromSection2,wecanagainobtainaconfidenceset forμwith
coverageprobability at least 1−α as shown inourprevious article [13]. Inpracticehowever, this
confidenceset ishard tocalculatesince σ̂2= σ̂2(ζ)has tobecalculatedforeveryζ∈S1.Thoughthese
confidencesetscanbeapproximatedbyusingagridas in [13],wesuggestusingasimultaneousupper
boundfor thevarianceof Imζ−1Zk.
429
Differential Geometrical Theory of Statistics
- Title
- Differential Geometrical Theory of Statistics
- Authors
- Frédéric Barbaresco
- Frank Nielsen
- Editor
- MDPI
- Location
- Basel
- Date
- 2017
- Language
- English
- License
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03842-425-3
- Size
- 17.0 x 24.4 cm
- Pages
- 476
- Keywords
- Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
- Categories
- Naturwissenschaften Physik