Operator-stable and operator-self-similar random fields
نویسندگان
چکیده
منابع مشابه
Multivariate Operator-Self-Similar Random Fields
Multivariate random fields whose distributions are invariant under operatorscalings in both time-domain and state space are studied. Such random fields are called operator-self-similar random fields and their scaling operators are characterized. Two classes of operator-self-similar stable random fields X = {X(t), t ∈ R} with values in R are constructed by utilizing homogeneous functions and sto...
متن کاملHölder Regularity for Operator Scaling Stable Random Fields
Abstract. We investigate the sample paths regularity of operator scaling α-stable random fields. Such fields were introduced in [6] as anisotropic generalizations of self-similar fields and satisfy the scaling property {X(cx);x ∈ R} (fdd) = {cX(x);x ∈ R} where E is a d× d real matrix and H > 0. In the case of harmonizable operator scaling random fields, the sample paths are locally Hölderian an...
متن کاملMulti-operator Scaling Random Fields
In this paper, we define and study a new class of random fields called harmonizable multi-operator scaling stable random fields. These fields satisfy a local asymptotic operator scaling property which generalizes both the local asymptotic self-similarity property and the operator scaling property. Actually, they locally look like operator scaling random fields whose order is allowed to vary alo...
متن کاملOperator Self-similar Processes on Banach Spaces
Operator self-similar (OSS) stochastic processes on arbitrary Banach spaces are considered. If the family of expectations of such a process is a spanning subset of the space, it is proved that the scaling family of operators of the process under consideration is a uniquely determinedmultiplicative group of operators. If the expectation-function of the process is continuous, it is proved that th...
متن کاملParameter estimation for operator scaling random fields
Operator scaling random fields are useful for modeling physical phenomena with different scaling properties in each coordinate. This paper develops a general parameter estimation method for such fields which allows an arbitrary set of scaling axes. The method is based on a new approach to nonlinear regression with errors whose mean is not zero. © 2013 Elsevier Inc. All rights reserved.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Stochastic Processes and their Applications
سال: 2019
ISSN: 0304-4149
DOI: 10.1016/j.spa.2018.11.013