Next Article in Journal
Sensitivity Analysis of an OLS Multiple Regression Inference with Respect to Possible Linear Endogeneity in the Explanatory Variables, for Both Modest and for Extremely Large Samples
Previous Article in Journal
Distributions You Can Count On …But What’s the Point?
Open AccessArticle

Mahalanobis Distances on Factor Model Based Estimation

Department of Economics and Statistics, Linnaeus university, 351 95 Växjö, Sweden
Econometrics 2020, 8(1), 10; https://doi.org/10.3390/econometrics8010010
Received: 20 August 2019 / Revised: 28 February 2020 / Accepted: 2 March 2020 / Published: 5 March 2020
A factor model based covariance matrix is used to build a new form of Mahalanobis distance. The distribution and relative properties of the new Mahalanobis distances are derived. A new type of Mahalanobis distance based on the separated part of the factor model is defined. Contamination effects of outliers detected by the new defined Mahalanobis distances are also investigated. An empirical example indicates that the new proposed separated type of Mahalanobis distances predominate the original sample Mahalanobis distance. View Full-Text
Keywords: dimension reduction; covariance matrix estimation; outlier detection; multivariate analysis dimension reduction; covariance matrix estimation; outlier detection; multivariate analysis
Show Figures

Figure 1

MDPI and ACS Style

Dai, D. Mahalanobis Distances on Factor Model Based Estimation. Econometrics 2020, 8, 10.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop