Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

Giuseppe Boari; Marta Nai Ruscone

Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

Giuseppe Boari, Marta Nai Ruscone

Risultato della ricerca: Contributo in libro › Contributo a convegno

Abstract

The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheffé, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.

Lingua originale	English
Titolo della pubblicazione ospite	Analysis and Modeling of Complex Data in Behavioural and Social Sciences. Book of Short Paper
Pagine	1-4
Numero di pagine	4
Stato di pubblicazione	Pubblicato - 2012
Evento	JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group - Anacapri Durata: 3 set 2012 → 4 set 2012

Convegno

Convegno	JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group
Città	Anacapri
Periodo	3/9/12 → 4/9/12

Keywords

cluster analysis
intraclass correlation coefficient

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title = "Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption",

abstract = "The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheff{\'e}, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.",

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year = "2012",

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booktitle = "Analysis and Modeling of Complex Data in Behavioural and Social Sciences. Book of Short Paper",

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AU - Boari, Giuseppe

AU - Nai Ruscone, Marta

PY - 2012

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N2 - The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheffé, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.

AB - The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheffé, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.

KW - cluster analysis

KW - intraclass correlation coefficient

KW - cluster analysis

KW - intraclass correlation coefficient

UR - http://hdl.handle.net/10807/31690

M3 - Conference contribution

SN - 978-88-6129-916-0

SP - 1

EP - 4

BT - Analysis and Modeling of Complex Data in Behavioural and Social Sciences. Book of Short Paper

T2 - JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group

Y2 - 3 September 2012 through 4 September 2012

ER -

Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

Abstract

Convegno

Keywords

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