New pages
From StatWiki
(Latest | Earliest) View (newer 50) (older 50) (20 | 50 | 100 | 250 | 500)- 20:54, 9 June 2011 Dissertation prm (hist) [2,291 bytes] Eisber (Talk | contribs) (New page: = prm =)
- 19:54, 26 January 2009 Mathematische Statistik - Übung 2.22 (hist) [1,081 bytes] Eisber (Talk | contribs) (New page: Manchmal ist die Hypothese von Interesse, dass Erwartungswert und Varianz von normalverteilten Zufallsgrössen in einem bestimmten Zusammenhang stehen. Wir betrachten daher Hypothesen der ...)
- 18:17, 22 January 2009 Mathematische Statistik - Bayes-Verfahren (hist) [2,741 bytes] Eisber (Talk | contribs) (New page: = Bayes-Verfahren = Erinnere Kapitel 2 / Entscheidungstheorie Verlustfunktion <math> l(\theta, a) \quad l:\Theta:A \rightarrow \mathbb{R}^{+} \,</math> z.b. wenn <math> \hat \theta ...)
- 17:57, 22 January 2009 Mathematische Statistik - ML-Prinzip und Informationstheorie (hist) [8,189 bytes] Eisber (Talk | contribs) (New page: = ML-Prinzip und Informationstheorie = '''Entropie von X''' <math> D(\theta_0, \theta) := - \operatorname{E}_{\theta_0} \log p_\theta(x)\,</math> '''Gegenseitige Entropie (mutual entro...)
- 17:56, 22 January 2009 Mathematische Statistik - Schätzverfahren / Maximum Likelihood (hist) [2,129 bytes] Eisber (Talk | contribs) (New page: == Maximum Likelihood == Betrachten reguläres parametrisches Modell mit Dichten (W-Fkt.) <math> \{ p_\theta(x) | \theta \in \Theta \}\,</math> mit <math> \Theta \subseteq \mathbb{R}^d, x ...)
- 17:56, 22 January 2009 Mathematische Statistik - Schätzverfahren / Heuristische Schätzprinzipien (hist) [5,541 bytes] Eisber (Talk | contribs) (New page: == 6.1 Heuristische Schätzprinzipien == Betrachten Beobachtung <math> X \in \mathcal{X}, X \sim \operatorname P \in \mathcal P, \mathcal P= \{P_\theta | \theta \in \Theta \}\,</math> Wi...)
- 09:52, 19 January 2009 Zeitreihenanalyse - Übung 10 (hist) [921 bytes] Eisber (Talk | contribs) (New page: = GDPC1 = <R output="display"> pdf(rpdf) GDPC1 <- readdataSK("GDPC1.txt","table") GDP <- ts(data=GDPC1[,2]) d <- diff(log(GDP),k=-1); n <- length(d) m <- floor(n/2) d.dm <- d-mean(d) ...)
