LEADER 00000nam a2200385 i 4500
001 CR9780511804373
003 UkCbUP
005 20181023134839.0
006 m o d
007 cr nn 008maaau
008 101021s1972 enk s 0 eng d
020 9780511804373|q(electronic bk.)
020 9780521083546|q(hardback)
020 9780521090957|q(paper)
040 UkCbUP|beng|erda|cUkCbUP|dGP
041 0 eng
050 4 QA273.43|b.R68 1972
082 04 519.2|218
100 1 Roussas, George G.,|eauthor
245 10 Contiguity of probability measures: some applications in
statistics /|cGeorge G. Roussas
264 1 Cambridge :|bCambridge University Press,|c1972
300 1 online resource (xiii, 248 pages) :|bdigital, PDF
file(s)
336 text|btxt|2rdacontent
337 unmediated|bn|2rdamedia
338 volume|bnc|2rdacarrier
347 text file|bPDF|2rda
490 1 Cambridge tracts in mathematics ;|v63
500 Title from publisher's bibliographic system (viewed on 05
Oct 2015)
505 0 On the concept of contiguity and related theorems --
Asymptotic expansions and asymptotic distribution of
likelihood functions -- Approximation of a given family of
probability measures by an exponential family, asymptotic
sufficiency -- Some statistical applications, AUMP and
AUMPU test for certain testing hypotheses problems -- Some
statistical applications, asymptotic efficiency of
estimates -- Multiparameter asymptotically optimal tests
520 This Tract presents an elaboration of the notion of
'contiguity', which is a concept of 'nearness' of
sequences of probability measures. It provides a powerful
mathematical tool for establishing certain theoretical
results with applications in statistics, particularly in
large sample theory problems, where it simplifies
derivations and points the way to important results. The
potential of this concept has so far only been touched
upon in the existing literature, and this book provides
the first systematic discussion of it. Alternative
characterizations of contiguity are first described and
related to more familiar mathematical ideas of a similar
nature. A number of general theorems are formulated and
proved. These results, which provide the means of
obtaining asymptotic expansions and distributions of
likelihood functions, are essential to the applications
which follow
650 0 Probability measures
650 0 Mathematical statistics
830 0 Cambridge tracts in mathematics ;|v63
856 40 |uhttps://doi.org/10.1017/CBO9780511804373