{"product_id":"asymptotic-theory-of-nonlinear-regression-9780792343356","title":"Asymptotic Theory of Nonlinear Regression","description":"\u003cp\u003e • Author(s): A. A. Ivanov\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eLet 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  i = {1R1, 8, Pi(), () E e} a statistical experiment generated by the observation Xi. n We shall say that a statistical experiment  n = {lRn, 8, P;, () E e} is the product of the statistical experiments  i, 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  n is generated by n independent observations X = (X1, ..., Xn). In this book we study the statistical experiments  n 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 ().\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Hardcover","offer_id":45283840917655,"sku":"9780792343356","price":7345.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780792343356.webp?v=1769306054","url":"https:\/\/atlanticbooks.com\/products\/asymptotic-theory-of-nonlinear-regression-9780792343356","provider":"Atlantic Books","version":"1.0","type":"link"}