A note on filtering for long memory processes

A. Thavaneswaran; C. C. Heyde

doi:10.1016/S0895-7177(01)00122-4

A note on filtering for long memory processes

A. Thavaneswaran^*, C. C. Heyde

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

This paper illustrates the use of quasilikelihood methods of inference for a class of possibly long-memory processes such as H-sssi (self-similar stationary increments) processes and long-range dependent sequences. In particular, they can be used in a general derivation without assuming normality of the process; this extends the result of Gripenberg and Norros [1]. Recursive filtering for models with linear intensity is also discussed in some detail.

Original language	English
Pages (from-to)	1139-1144
Number of pages	6
Journal	Mathematical and Computer Modelling
Volume	34
Issue number	9-11
DOIs	https://doi.org/10.1016/S0895-7177(01)00122-4
Publication status	Published - 24 Sept 2001
Externally published	Yes

Access to Document

10.1016/S0895-7177(01)00122-4

Cite this

@article{a6e1a5d5d0fb412fb66968f4ab10d0bc,

title = "A note on filtering for long memory processes",

abstract = "This paper illustrates the use of quasilikelihood methods of inference for a class of possibly long-memory processes such as H-sssi (self-similar stationary increments) processes and long-range dependent sequences. In particular, they can be used in a general derivation without assuming normality of the process; this extends the result of Gripenberg and Norros [1]. Recursive filtering for models with linear intensity is also discussed in some detail.",

keywords = "Estimating function, Filtering, Long-memory, Prediction",

author = "A. Thavaneswaran and Heyde, {C. C.}",

year = "2001",

month = sep,

day = "24",

doi = "10.1016/S0895-7177(01)00122-4",

language = "English",

volume = "34",

pages = "1139--1144",

journal = "Mathematical and Computer Modelling",

issn = "0895-7177",

publisher = "Elsevier Ltd.",

number = "9-11",

}

TY - JOUR

T1 - A note on filtering for long memory processes

AU - Thavaneswaran, A.

AU - Heyde, C. C.

PY - 2001/9/24

Y1 - 2001/9/24

N2 - This paper illustrates the use of quasilikelihood methods of inference for a class of possibly long-memory processes such as H-sssi (self-similar stationary increments) processes and long-range dependent sequences. In particular, they can be used in a general derivation without assuming normality of the process; this extends the result of Gripenberg and Norros [1]. Recursive filtering for models with linear intensity is also discussed in some detail.

AB - This paper illustrates the use of quasilikelihood methods of inference for a class of possibly long-memory processes such as H-sssi (self-similar stationary increments) processes and long-range dependent sequences. In particular, they can be used in a general derivation without assuming normality of the process; this extends the result of Gripenberg and Norros [1]. Recursive filtering for models with linear intensity is also discussed in some detail.

KW - Estimating function

KW - Filtering

KW - Long-memory

KW - Prediction

UR - http://www.scopus.com/inward/record.url?scp=0035943849&partnerID=8YFLogxK

U2 - 10.1016/S0895-7177(01)00122-4

DO - 10.1016/S0895-7177(01)00122-4

M3 - Article

SN - 0895-7177

VL - 34

SP - 1139

EP - 1144

JO - Mathematical and Computer Modelling

JF - Mathematical and Computer Modelling

IS - 9-11

ER -

A note on filtering for long memory processes

Abstract

Access to Document

Other files and links

Fingerprint

Cite this