By G. Velo, A. S. Wightman
ISBN-10: 3540086552
ISBN-13: 9783540086550
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Extra resources for Invariant Wave Equations
Sample text
NB , j=1 i=1 unter den Nebenbedingungen nB ∑ wi j = wi. , nA und j=1 nA ∑ wi j = w. , nB , i=1 wobei di j die Distanz zwischen den Datensätzen i und j beinhaltet und wi j eine mögliche Gewichtung darstellt. Liu und Kovacevic (1998) beschreiben die so genannte „generalized rank weightsplit method“ zur Lösung dieses Optimierungsproblems. Rubin (1986) beschreibt darüber hinaus, wie die Ausgangstabellen aufzuteilen sind und wie die Datensätze anhand ihrer z-Werte61 geordnet werden müssen, um erfolgreiches constrained 58 59 60 61 Vgl.
4 stellt den traditionellen Matching-Prozess dar. Television Panel Consumer Panel Unit no. Gender Age ... TV viewing behavior Unit no. Gender Age ... Consuming behavior 1 ... ... 1 ... ... ... ... ... ... ... i 1 38 ... j 1 36 ... ... ... ... ... ... nA ... ... nB ... ... Fusion Sample Unit no. Gender Age ... TV viewing behavior Consuming behavior 1 ... ... ... ... ... ... i 1 38 ... ... ... ... ... nA ... ... ... 4: Traditioneller Ansatz des statistischen Matchings (vgl.
I 1 38 ... ... ... ... ... nA ... ... ... 4: Traditioneller Ansatz des statistischen Matchings (vgl. [R04a], S. ) Man betrachte die Variablen Z, X und Y als multivariate Zufallsvariablen mit einer gemeinsamen Wahrscheinlichkeit oder Dichtefunktion fZ,X,Y . 25 Sei fZ,X,Y die Dichte der Datensatzmenge nach dem Matching, d. h. nach Hinzufügen der zusätzlichen Attribute der statistischen Zwillinge. 27 Sie besagt, dass die 24 25 26 27 Vgl. [HIT98], S. 263. ¨ S. 20. Vgl.
Invariant Wave Equations by G. Velo, A. S. Wightman
by Edward
4.4
