Distribuţia Binomială: Modelare Statistică, Optimizare Numerică, cu ...
Distribuţia Binomială: Modelare Statistică, Optimizare Numerică, cu ...
Distribuţia Binomială: Modelare Statistică, Optimizare Numerică, cu ...
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
<strong>Distribuţia</strong> <strong>Binomială</strong>: <strong>Modelare</strong> <strong>Statistică</strong>, <strong>Optimizare</strong> <strong>Numerică</strong>, <strong>cu</strong> Aplicaţii în Bioinformatică şi Biochimie<br />
this->Xi = (this->X==0 ? 0 : pow(sin(tX-t0),2));<br />
this->Xs = (this->X==this->n ? 1 : pow(sin(tX+t0),2));<br />
}<br />
function ArcS_C(){<br />
tX = asin(pow((this->X+3/8)/(this->n+3/4),0.5));<br />
t0 = this->z/pow(4*this->n,0.5);<br />
this->Xi = (this->X==0 ? 0 : pow(sin(tX-t0),2));<br />
this->Xs = (this->X==this->n ? 1 : pow(sin(tX+t0),2));<br />
}<br />
function ArcS_D(){<br />
t0 = this->z/pow(4*this->n,0.5);<br />
this->Xi = asin(pow((this->X-0.5)/this->n,0.5));<br />
this->Xs = asin(pow((this->X+0.5)/this->n,0.5));<br />
this->Xi = (this->X==0 ? 0 : pow(sin(this->Xi-t0),2));<br />
this->Xs = (this->X==this->n ? 1 : pow(sin(this->Xs+t0),2));<br />
}<br />
function ArcS_E(){<br />
t0 = this->z/pow(4*this->n+2,0.5);<br />
this->Xi = asin(pow((this->X+3/8-0.5)/(this->n+3/4),0.5));<br />
this->Xs = asin(pow((this->X+3/8+0.5)/(this->n+3/4),0.5));<br />
this->Xi = (this->X==0 ? 0 : pow(sin(this->Xi-t0),2));<br />
this->Xs = (this->X==this->n ? 1 : pow(sin(this->Xs+t0),2));<br />
}<br />
÷ Formule matematice:<br />
Intervalul de încredere Logit<br />
CILogit_C(X,m) =<br />
÷ Algoritmi de cal<strong>cu</strong>l:<br />
CILogit_N(X,m) =<br />
1<br />
1 −<br />
X ⎛<br />
1+<br />
exp⎜<br />
⎜<br />
± z<br />
m − X ⎝<br />
1 −<br />
1 ⎛<br />
X + ⎜<br />
1+<br />
2 exp<br />
⎜<br />
± z<br />
1 ⎜<br />
m − X + ⎜<br />
2 ⎝<br />
39(157)<br />
m ⎞<br />
⎟<br />
X(<br />
m − X)<br />
⎟<br />
⎠<br />
1<br />
⎞<br />
⎟<br />
( m + 1)(<br />
m + 2)<br />
⎟<br />
⎛ 1 ⎞⎛<br />
1 ⎞ ⎟<br />
m⎜X<br />
+ ⎟⎜m<br />
− X + ⎟ ⎟<br />
⎝ 2 ⎠⎝<br />
2 ⎠ ⎠<br />
this->X_n = X/n;<br />
function Logit_N(){<br />
if((this->X==0)||(this->X==this->n)) BetaC01();<br />
else{<br />
t0 = -log(1/this->X_n-1);<br />
t1 = this->z * pow(Wald_0,-0.5);<br />
this->Xi = t0-t1;<br />
this->Xs = t0+t1;<br />
this->Xi = exp(this->Xi)/(1+exp(this->Xi));<br />
this->Xs = exp(this->Xs)/(1+exp(this->Xs));<br />
}<br />
}<br />
function Logit_C(){<br />
t0 = log((this->X+0.5)/(this->n-this->X+0.5));<br />
t1 = this->z * pow((this->n+1)*(this->n+2)/this->n/<br />
(this->X+1)/(this->n-this->X+1),0.5);