EconPapers    
Economics at your fingertips  
 

Inference in the Growth Curve Model under Multivariate Skew Normal Distribution

Sayantee Jana, Narayanaswamy Balakrishnan and Jemila S. Hamid ()
Additional contact information
Sayantee Jana: McMaster University
Narayanaswamy Balakrishnan: McMaster University
Jemila S. Hamid: McMaster University

Sankhya B: The Indian Journal of Statistics, 2020, vol. 82, issue 1, No 2, 34-69

Abstract: Abstract Existing methods for estimating the parameters of the Growth Curve Model (GCM) rely on the assumption that the underlying distribution for the error terms is multivariate normal. However, we often come across skewed data in practical applications; and estimators developed under the normality assumption may not be valid in such situations. Simulation studies conducted in this paper, in fact, show that existing methods are sensitive to skewness, where normal based estimators are associated with increased bias and mean squared error (MSE), when the normality assumption is violated. Methods appropriate for skewed distributions are, therefore, required. In this paper, estimators for the mean and covariance matrices of the GCM under multivariate skew normal (MSN) distribution are proposed. An estimator for the additional skewness parameter of the MSN distribution is also provided. The estimators are derived using the expectation maximization (EM) algorithm and extensive simulations are performed to examine the performance of the estimators. Comparisons with existing estimators show that our estimators perform better than the existing estimators, when the underlying distribution is multivariate skew normal. Illustration using real data set is also provided.

Keywords: Growth curve model; Multivariate skew normal distribution; EM algorithm; Longitudinal analysis; Matrix estimation.; Primary: 62H12; 62H10; Secondary: 65C99; 62F10. (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s13571-018-0174-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankhb:v:82:y:2020:i:1:d:10.1007_s13571-018-0174-1

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-018-0174-1

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2020-09-19
Handle: RePEc:spr:sankhb:v:82:y:2020:i:1:d:10.1007_s13571-018-0174-1