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Mansoor, Rashid
Publications (6 of 6) Show all publications
Mansoor, R. (2013). Assessing Distributional Properties of High-Dimensional Data. (Doctoral dissertation). Jönköping: Jönköping International Business School
Open this publication in new window or tab >>Assessing Distributional Properties of High-Dimensional Data
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This doctoral thesis consists of five papers in the field of multivariate statistical analysis of high-dimensional data. Because of the wide application and methodological scope, the individual papers in the thesis necessarily target a number of different statistical issues. In the first paper, Monte Carlo simulations are used to investigate a number of tests of multivariate non-normality with respect to their increasing dimension asymptotic (IDA) properties as the dimension p grows proportionally with the number of observations n such that p/n → c where is a constant. In the second paper a new test for non-normality that utilizes principal components is proposed for cases when p/n → c. The power and size of the test are examined through Monte Carlo simulations where different combinations of p and n are used.

The third paper treats the problem of the relation between the second central moment of a distribution to its first raw moment. In order to make inference of the systematic relationship between mean and standard deviation, a model that captures this relationship by a slope parameter (β) is proposed and three different estimators of this parameter are developed and their consistency proven in the context where the number of variables increases proportionally to the number of observations. In the fourth paper, a Bayesian regression approach has been taken to model the relationship between the mean and standard deviation of the excess return and to test hypotheses regarding the β parameter. An empirical example involving Stockholm exchange market data is included. Then finally in the fifth paper three new methods to test for panel cointegration

Place, publisher, year, edition, pages
Jönköping: Jönköping International Business School, 2013. p. 27
Series
JIBS Dissertation Series, ISSN 1403-0470 ; 092
National Category
Economics and Business
Identifiers
urn:nbn:se:hj:diva-22547 (URN)978-91-86345-46-4 (ISBN)
Public defence
2013-11-29, B1014, Jönköping International Business School, Gjuterigatan 5, 10:00 (English)
Opponent
Supervisors
Available from: 2013-11-07 Created: 2013-11-07 Last updated: 2013-11-07Bibliographically approved
Holgersson, T. & Mansoor, R. (2013). Assessing Normality of High-Dimensional Data. Communications in statistics. Simulation and computation, 42(2), 360-369
Open this publication in new window or tab >>Assessing Normality of High-Dimensional Data
2013 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 2, p. 360-369Article in journal (Refereed) Published
Abstract [en]

The assumption of normality is crucial in many multivariate inference methods and may be even more important when the dimension of data is proportional to the sample size. It is therefore necessary that tests for multivariate non normality remain well behaved in such settings. In this article, we examine the properties of three common moment-based tests for non normality under increasing dimension asymptotics (IDA). It is demonstrated through Monte Carlo simulations that one of the tests is inconsistent under IDA and that one of them stands out as uniformly superior to the other two.

Keywords
Multivariate skewness and kurtosis, Increasing dimension, Asymptotics, Non normality
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-16439 (URN)10.1080/03610918.2011.636164 (DOI)2-s2.0-84870900232 (Scopus ID)
Available from: 2011-10-24 Created: 2011-10-24 Last updated: 2019-02-21Bibliographically approved
Mansoor, R. & Holgersson, T. (2012). Testing for Autocorrelation in High-dimensional Data. Jönköping: Jönköping International Business School
Open this publication in new window or tab >>Testing for Autocorrelation in High-dimensional Data
2012 (English)Report (Other academic)
Place, publisher, year, edition, pages
Jönköping: Jönköping International Business School, 2012. p. 15
Series
JIBS Working Paper Series, ISSN 1403-0454 ; 2012:2
National Category
Natural Sciences
Identifiers
urn:nbn:se:hj:diva-17806 (URN)
Available from: 2012-03-15 Created: 2012-03-15 Last updated: 2015-09-21
Mansoor, R.A Bayesian Approach for Estimating Mean-Standard Deviation Ratios of Financial Data.
Open this publication in new window or tab >>A Bayesian Approach for Estimating Mean-Standard Deviation Ratios of Financial Data
(English)Manuscript (preprint) (Other academic)
National Category
Economics and Business
Identifiers
urn:nbn:se:hj:diva-22545 (URN)
Available from: 2013-11-07 Created: 2013-11-07 Last updated: 2013-11-07Bibliographically approved
Mansoor, R. & Månsson, K.Testing for Panel Cointegration in High-Dimensional Data in the Presence of Cross-Sectional Dependency.
Open this publication in new window or tab >>Testing for Panel Cointegration in High-Dimensional Data in the Presence of Cross-Sectional Dependency
(English)Manuscript (preprint) (Other academic)
National Category
Economics and Business
Identifiers
urn:nbn:se:hj:diva-22546 (URN)
Available from: 2013-11-07 Created: 2013-11-07 Last updated: 2013-11-07Bibliographically approved
Mansoor, R.Using Principal Components to Test Normality of High-Dimensional Data.
Open this publication in new window or tab >>Using Principal Components to Test Normality of High-Dimensional Data
(English)Manuscript (preprint) (Other academic)
National Category
Economics and Business
Identifiers
urn:nbn:se:hj:diva-22544 (URN)
Available from: 2013-11-07 Created: 2013-11-07 Last updated: 2013-11-07Bibliographically approved
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