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Asymptotic Theory of Nonlinear Regression (Paperback, Softcover reprint of the original 1st ed. 1997)
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Asymptotic Theory of Nonlinear Regression (Paperback, Softcover reprint of the original 1st ed. 1997)
Series: Mathematics and Its Applications, 389
Expected to ship within 10 - 15 working days
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Let us assume that an observation Xi is a random variable (r.v.)
with values in 1 1 (1R1 , 8 ) and distribution Pi (1R1 is the real
line, and 8 is the cr-algebra of its Borel subsets). Let us also
assume that the unknown distribution Pi belongs to a 1 certain
parametric family {Pi() , () E e}. We call the triple GBPi = {1R1 ,
8 , Pi(), () E e} a statistical experiment generated by the
observation Xi. n We shall say that a statistical experiment GBPn =
{lRn, 8 , P; ,() E e} is the product of the statistical experiments
GBPi, i = 1, ... ,n if PO' = P () X ... X P () (IRn 1 n n is the
n-dimensional Euclidean space, and 8 is the cr-algebra of its Borel
subsets). In this manner the experiment GBPn is generated by n
independent observations X = (X1, ... ,Xn). In this book we study
the statistical experiments GBPn generated by observations of the
form j = 1, ... ,n. (0.1) Xj = g(j, (}) + cj, c c In (0.1) g(j, (})
is a non-random function defined on e , where e is the closure in
IRq of the open set e ~ IRq, and C j are independent r. v .-s with
common distribution function (dJ.) P not depending on ().
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