zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
On a semiparametric regression model whose errors form a linear process with negatively associated innovations. (English) Zbl 1098.62044
Summary: We are concerned with the regression model y i =x i β+g(t i )+V i (1in), where the known design points (x i ,t i ), the unknown slope parameter β, and the nonparametric component g are non-random and where the correlated errors V i = j=- c j e i-j , with negatively associated e i , are random variables. Under appropriate conditions, we study the asymptotic normality for the least squares estimator of β and the nonparametric estimator of g(·). Moreover, strong convergence rates of these estimators are considered. Our results show that the nonparametric estimator of g(·) can attain the optimal convergence rate.
MSC:
62G08Nonparametric regression
62G20Nonparametric asymptotic efficiency
62F12Asymptotic properties of parametric estimators