@book{WACHTER:1978:SSOHSS,
   AUTHOR = {Wachter, Kenneth W. and Hammel, E.A. and Laslett, P.},
    TITLE = {Statistical Studies of Historical Social Structure},
     YEAR = {1978},
     ISBN = {0127291504, 9780127291505},
PUBLISHER = {Academic Press},
  ADDRESS = {New York, NY},
   }

        

@article{WACHTER:1978:TSLORM:MR0467894,
   AUTHOR = {Wachter, K.W.},
    TITLE = {The strong limits of random matrix spectra for sample matrices of
             independent elements},
  JOURNAL = {Ann. Probab.},
     ISSN = {0091-1798},
   VOLUME = {6},
   NUMBER = {1},
     YEAR = {1978},
    PAGES = {1--18},
  MRCLASS = {60F15},
MRREVIEWER = {V. L. Girko},
 ZBLCLASS = {60F15;62H25},
       ID = {info:zbl/0374.60039, info:mr/MR0467894 (57 \#7744),
             info:doi/10.1214/aop/1176995607},
 ABSTRACT = {This paper proves almost-sure convergence of the empirical measure
             of the normalized singular values of increasing rectangular
             submatrices of an infinite random matrix of independent elements.
             The limit is the limit as both dimensions grow large in some ratio.
             The matrix elements are required to have uniformly bounded central
             $2 + \delta$th moments, and the same means and variances within a
             row. The first section (relaxing the restriction on variances)
             proves any limit-in-distribution to be a constant measure rather
             than a random measure, establishes the existence of subsequences
             convergent in probability, and gives a criterion for almost-sure
             convergence. The second section proves the almost-sure limit to
             exist whenever the distribution of the row variances converges. It
             identifies the limit as a nonrandom probability measure which may
             be evaluated as a function of the limiting distribution of row
             variances and the dimension ratio. These asymptotic formulae
             underlie recently developed methods of probability plotting for
             principal components and have applications to multiple discriminant
             ratios and other linear multivariate statistics.},
   }