Over the recent years, great interest has been addressed to ordinal variables and to the development of multivariate statistical techniques for their analysis. The empirical comparison of such techniques, either exploratory or inferential, often requires simulating ordinal data under different experimental conditions. Several methods for generating multidimensional data from continuous or discrete variables have been proposed. This paper focuses on an algorithm for generating ordinal data with assigned marginal distributions and correlation matrix. The procedure consists of two steps: the first one aims at setting up the desired experimental conditions, employing a straightforward discretization procedure from a standard multinormal variable, whose correlation matrix is computed through an iterative algorithm in order to achieve the target correlation matrix for ordinal data. The second step actually implements the sampling under the experimental conditions and allows performing a Monte Carlo simulation study. The algorithm does not suffer from some drawbacks encountered by other existing techniques and has a large application. It is implemented in R through a function which allows the user to choose the sample of size, the number of variables, their distribution and the target correlation matrix which is also checked for its feasibility. Two examples of application are provided.

Generation of multivariate discrete data / A. Barbiero, P.A. Ferrari - In: Book of Abstracts: 4. International Conference On Computational and Financial Econometrics (CFE 10) and 3. International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computing & Statistics (ERCIM 10)[s.l] : ERCIM, 2010 Dec. - pp. 41-41 (( Intervento presentato al 3. convegno International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computing & Statistics (ERCIM 10) tenutosi a London nel 2010.

Generation of multivariate discrete data

A. Barbiero
Primo
;
P.A. Ferrari
Ultimo
2010

Abstract

Over the recent years, great interest has been addressed to ordinal variables and to the development of multivariate statistical techniques for their analysis. The empirical comparison of such techniques, either exploratory or inferential, often requires simulating ordinal data under different experimental conditions. Several methods for generating multidimensional data from continuous or discrete variables have been proposed. This paper focuses on an algorithm for generating ordinal data with assigned marginal distributions and correlation matrix. The procedure consists of two steps: the first one aims at setting up the desired experimental conditions, employing a straightforward discretization procedure from a standard multinormal variable, whose correlation matrix is computed through an iterative algorithm in order to achieve the target correlation matrix for ordinal data. The second step actually implements the sampling under the experimental conditions and allows performing a Monte Carlo simulation study. The algorithm does not suffer from some drawbacks encountered by other existing techniques and has a large application. It is implemented in R through a function which allows the user to choose the sample of size, the number of variables, their distribution and the target correlation matrix which is also checked for its feasibility. Two examples of application are provided.
Settore SECS-S/01 - Statistica
dic-2010
Queen Mary, University of London
Birkbeck, University of London
London School of Economics
http://www.cfe-csda.org/cfe10/LondonBoA.pdf
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
LondonBoA.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 1.04 MB
Formato Adobe PDF
1.04 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/169750
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact