CONTENTS Introduction.......................................................................................................................................................................... 5 I. Infinitely divisible probability measures on $R^d$....................................................................................... 6 II. The classical limit theorems for sums of independent random vectors................................................ 14 III. Convergence in law to ℒ ($\overrightarrow a$, A, µ) for sums of dependent random vectors.......... 21 IV. Convergence in law to l ($\overrightarrow a$, A, ν) for sums of dependent random vectors............ 84 V. Convergence in law to K($\overrightarrow m$, A, ϰ) for sums of dependent random vectors with finite variances............................................................................................................................................... 47 VI. Particular cases of limit distributions........................................................................................................... 40 VII. Another method of conditioning.................................................................................................................... 57 References........................................................................................................................................................................... 58
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