ABSTRACT
Linear algebra is used extensively in statistical science, in particular in linear statistical models, see, e.g., Graybill [Gra01], Ravishanker and Dey [RD02], Rencher [Ren00], Searle [Sea97], Seber and Lee [SL03], Sengupta and Jammalamadaka [SJ03], and Stapleton [Sta95]; as well as in applied economics, see, e.g., Searle and Willett [SW01]; econometrics, see, e.g., Davidson and MacKinnon [DM04], Magnus and Neudecker [MN99], and Rao and Rao [RR98]; Markov chain theory, see, e.g., Chapter 54 or Kemeny and Snell [KS83]; multivariate statistical analysis, see, e.g., Anderson [And03], Kollo and von Rosen [KvR05], and Seber [Seb04];psychometrics, see,e.g., Takane [Tak04] and Takeuchi, Yanai, and Mukherjee [TYM82]; and random matrix theory, see, e.g., Bleher and Its [BI01] and Mehta [Meh04]. Moreover, there are several books on linear algebra and matrix theory written by (and mainly for) statisticians, see, e.g., Bapat [Bap00], Graybill [Gra83], Hadi [Had96], Harville [Har97], [Har01], Healy [Hea00], Rao and Rao [RR98], Schott [Sch05], Searle [Sea82], and Seber [Seb06].
