Confidence Interval for the complexity index of functional data

Enea Giuseppe BONGIORNO; Kwo Lik CHAN; Aldo GOIA

Confidence Interval for the complexity index of functional data

Enea Giuseppe BONGIORNO, Kwo Lik CHAN, Aldo GOIA

Department of Economics and Business Studies

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper presents the concept of complexity index for generic random processes. This is based on the notion of small-ball probability and the possibility that this probability can be decomposed as a factorization of two functions. One of these two factors carries information regarding the complexity of the process, which coincides with the number of random sources characterizing the process or, equivalently, its degrees of freedom. This factor has been studied in literature and this paper shows some statistical results on how it can be exploited to make inference on the degrees of freedom of the random process.

Original language	English
Number of pages	4
Publication status	Accepted/In press - 4 Nov 2025
Event	The 52nd Scientific Meeting of the Italian Statistical Society - Bari Duration: 3 Jan 0001 → …

Conference

Conference	The 52nd Scientific Meeting of the Italian Statistical Society
City	Bari
Period	3/01/01 → …

Keywords

Small-ball probability
1–approximating functional
β–mixing
degrees of freedom

Access to Document

https://hdl.handle.net/11579/182282

Cite this

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title = "Confidence Interval for the complexity index of functional data",

abstract = "This paper presents the concept of complexity index for generic random processes. This is based on the notion of small-ball probability and the possibility that this probability can be decomposed as a factorization of two functions. One of these two factors carries information regarding the complexity of the process, which coincides with the number of random sources characterizing the process or, equivalently, its degrees of freedom. This factor has been studied in literature and this paper shows some statistical results on how it can be exploited to make inference on the degrees of freedom of the random process.",

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AU - CHAN, Kwo Lik

AU - GOIA, Aldo

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N2 - This paper presents the concept of complexity index for generic random processes. This is based on the notion of small-ball probability and the possibility that this probability can be decomposed as a factorization of two functions. One of these two factors carries information regarding the complexity of the process, which coincides with the number of random sources characterizing the process or, equivalently, its degrees of freedom. This factor has been studied in literature and this paper shows some statistical results on how it can be exploited to make inference on the degrees of freedom of the random process.

AB - This paper presents the concept of complexity index for generic random processes. This is based on the notion of small-ball probability and the possibility that this probability can be decomposed as a factorization of two functions. One of these two factors carries information regarding the complexity of the process, which coincides with the number of random sources characterizing the process or, equivalently, its degrees of freedom. This factor has been studied in literature and this paper shows some statistical results on how it can be exploited to make inference on the degrees of freedom of the random process.

KW - Small-ball probability

KW - 1–approximating functional

KW - β–mixing

KW - degrees of freedom

KW - Small-ball probability

KW - 1–approximating functional

KW - β–mixing

KW - degrees of freedom

UR - https://iris.uniupo.it/handle/11579/182282.3

M3 - Paper

T2 - The 52nd Scientific Meeting of the Italian Statistical Society

Y2 - 3 January 0001

ER -

Confidence Interval for the complexity index of functional data

Abstract

Conference

Keywords

Access to Document

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