Optimization giving wildly different answers depending on box constraints

I am trying to fit a skew normal distribution to some data. I only have data from the left tail. My code is as follow:

using Optim
using Distributions
using QuadGK unction T(h, a)
    return 1/(2*π) * quadgk(x -> exp(-(1/2) * h^2 * (1+x^2)) / (1 + x^2), 0 , a)[1]
end

function skewnormalcdf(args, x)
    ξ, ω, α = args
    return @. cdf(Normal(ξ, ω), x) - 2 * T((x - ξ)/ω, α)
end

function skewcost(x)
    ξ, ω, α = x
    return sum((empiricalcdf .- skewnormalcdf([ξ, ω, α], collect(1:13))).^2) 
end

empiricalcdf =BigFloat[5.2909066017292616379563696682453155517578125e-17, 2.998035847356916860912894586658405236403970364197066576597176324261995183917406e-15, 2.893916126231428870186285918394794483524045786436811752754107927550555889872896e-12, 2.893916126231428870186285918394794483524045786436811752754107927550555889872896e-12, 5.984024927027286297718736980025470237800934633258129628285530100177506962477736e-11, 9.828060393355408458878449871353338972538513412036598513660056548532639730808569e-10, 1.306578361293908361005883734780968634960112544877883919057134339745908022112353e-08, 1.426321476573129163499158095212362359685712423190289439313890855808378017172133e-07, 1.291322552024760413190906159876634677313715744603532551931414495220685166587682e-06, 9.765050285646564493780162129498592850243974466341922554440277386489442472405909e-06, 6.197926915196306576612885050087411198022520616606169147964179640314236999394659e-05, 0.0003312274093856579135864289998536855534145377316597168805215086298514038385190639, 0.001493228789321792139431079924969027888633593921034312152826696867055363237815693]

I then run different optimizations, slightly varying the box constraint.

for i in 0.5:0.1:5
       res = optimize(skewcost, [-100.0, 0.01, -i], [100.0, 10.0, i], [0.0, 1.0, 0.0])
       print(i, " ", Optim.minimum(res), "\n")
end

This gives:

0.5 4.337097424258804861792584334740937857712109849288063017519229224922004079649252e-09
0.6 5.1821543534925344101899612836301844966882658580752592020492136602764512939768e-09
0.7 1.40492718489139474069100010374867146150911986075983943252882730190293674075533e-11
0.8 1.068130801871595462372296005777866561060835777925479111674990387217733899630118e-10
0.9 2.916118373940864220527601909879196352799409921233779418545835384493061320631925e-12
1.0 4.114063479495389995882494808456921266509896597604495516366778082815765217204441e-12
1.1 1.283124942550091086493349593936278766322030197355030198233324364027465299190772e-12
1.2 1.04191988167077517052998942235016873047702726226059701093568027966650220291751e-11
1.3 2.745317513091285627296920708669145573311390432906398171482720539544213323435897e-13
1.4 1.16418951208752561481889464181206453390919975569854514336878234457719063963556e-12
1.5 6.351032595434875504286882444881045336907308380944350366920398498227349854857981e-13
1.6 1.669456499895190746991196962748833185378922673635550772356298954983393493092299e-11
1.7 6.673040821354296127377618571820430031918447172757031348263465637441977761342772e-13
1.8 5.41987814523154058650399581616480199068048529911221970562523797332151913371414e-14
1.9 3.256292209504446603407638940194957284068344160141416449575471606599789603051439e-12
2.0 4.772111297418343088851366937851904497172882996782722568127808352860839328768979e-13
2.1 2.431260341547170974705459264121988138926290747760109731617228005635169410300702e-12
2.2 7.547105164295734417423299986665593449392952146791285668780747676212803349714907e-12
2.3 2.172501805059870515211933044799083616714788699120815152352534916981394734497903e-12
2.4 5.71027372242846628347840533950717970258872772069119435826078852769662713941485e-09
2.5 4.862843473961244567134854704527168803662852647360339596861399586272749240688299e-13
2.6 3.426754009409042500506176716180693151511540066055721381948993213449861254853354e-11
2.7 5.383511123847564035733625261659265854873809599963814723903525423018299057288747e-10
2.8 3.123034487588670432919056962216881983284338588905795997040476036503170438745448e-13
2.9 2.195683618890999760783854401011128285857034141139365889382417518773954701519229e-12
3.0 3.324759403344652752441181854249721350004967361633183384376595790222992138143735e-13
3.1 3.374915885409991403965019236184450168510353458446078253172888863461290599336321e-13
3.2 2.434337554927044905580056952267415838276811325130895649822303525268462425538114e-12
3.3 4.802074402430022703861059485522988853042105581966851634744434401280173464252615e-12
3.4 1.988371922560574987261854284970730309500424737493713815911608169029037666538586e-12
3.5 2.749253439701993126942752000643122555859437559070663787961561372617622149571722e-12
3.6 3.783236260783879340431030401627976768414191092583629841675287555995397581824257e-13
3.7 2.358895575868717462364486167749381208983803012193290196461358768246200164804414e-12
3.8 1.033683500366723327865878694111520807683920124743995958824215109150748802271321e-10
3.9 3.209187863352080549712170431270865404456012312458360313848557503301579700892068e-12
4.0 7.422008561624809585537930629158062105224282024702676917704379558537052974037055e-13
4.1 1.803121296995209225430863951139089471546423973504661087856434590154217990360054e-12
4.2 5.182483573441776133465684733796395559032817005696900544471928553828713273305454e-13
4.3 8.422128900049079464227026093804259043007005457048372272547952934397021837017561e-13
4.4 5.351140586564285462443561540482615223509503496661722969524010384740640650539294e-13
4.5 7.4994820281135230259072177782976131820045734646420746139859177207496543903621e-13
4.6 4.986181267406673860006957816642930183468217461298147187336990673010958339573762e-13
4.7 2.214163909825792215159470156111823423168348483196942827320198253726318881300034e-12
4.8 1.233922519787192611662903440358827640705326501550269909403249963234085982113462e-10
4.9 2.406336608394897702747626800837665473201310442134746131616591222312936940390304e-12
5.0 5.811347645553924505825189249151876279862349405694662298483118120716376904526018e-11

What is going on and how can I produce an optimization I trust for this problem?

Please don’t start yet another topic about

and

Instead of giving your problem more exposure (as you may expect), it just scatters solutions all over the place.

OK no problem. I thought the problems might be different enough but I will stick them all in the same place as you suggest.