Prasanta Chandra Mahalanobis

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The following article is from The Great Soviet Encyclopedia (1979). It might be outdated or ideologically biased.

Mahalanobis, Prasanta Chandra

 

Born June 29, 1893, in Calcutta; died there June 27, 1972. Indian statistician and economist.

Mahalanobis was educated at the University of Calcutta (1912) and at Cambridge (1915). From 1931 he headed the India Statistical Institute (Calcutta), which he had established. He was founder (1933) and editor of the statistical journal Sankhya. He served as a member of the Planning Commission of India (1955-67) and as a government advisor on problems of statistics (1949-67).

Mahalanobis’ theoretical works and practical recommendations proceed from positions of petit bourgeois radicalism. He called for the industrialization of India on a basis of accelerated development of the state sector and the centralization of material and monetary resources in the hands of the state, and he favored the simultaneous further development of small enterprise. Mahalanobis played an important part in the formation and development of economic and scientific ties between India and the USSR. He was a foreign member of the Academy of Sciences of the USSR (1958) and a member of the Royal Society of London (1945).

WORKS

Some Observations on the Process of the Growth of National Income. [Calcutta, 1953.]
Talks on Planning. Bombay-Calcutta [1961].
The Approach of Operational Research to Planning in India. Bombay-Calcutta [1963].
In Russian translation:
Vyborochnye obsledovaniia v Indii (Novyi opyt Indiiskogo statisticheskogo instituta). Moscow, 1958. (Translated from English.)
The Great Soviet Encyclopedia, 3rd Edition (1970-1979). © 2010 The Gale Group, Inc. All rights reserved.
References in periodicals archive ?
To perform this algorithm in Matlab software, three norms of Euclidean, Manhattan and Mahalonobis have been used to calculate the distance from the cluster center.
Os dados coletados foram tratados antes da realizacao dos testes estatisticos, e os outliers foram eliminados por meio do metodo de distancia de Mahalonobis conforme relatado em Reimann, Filzmoser, Garrett e Dutter (2010).
These methods are HSV color space, Euclidian distance with RGB color space and Mahalonobis distance with RGB color space.
In particular, multivariate normality was tested by assessing multivariate kurtosis, and multivariate outliers were examined through Mahalonobis distance (Ullman, 2006).
The best band set for the classifications was determined undertaking divergence statistical indicators and the classification of image was also accomplished by supervised method and by using maximum likelihood algorithm, Mahalonobis and the minimum distance from the mean.
Separate screening procedures (using Mahalonobis distances and [chi square] tests of significance) were conducted on the Sport-MPS and body-image data (i.e., the data to be included in the first canonical correlation analysis), and on the Hewitt-MPS and body-image data (i.e., the data to be included in the second canonical correlation analysis).
In this way, mahalonobis distance square is calculated for each data through the following mathematical relationship and its significant is also clear (Hair et al.
The selection criterion was the class with the shortest Mahalonobis distance between its centroid and the sample in canonical space.
Among the techniques used are Mahalonobis distances and a combination of factorial analysis and the algorithm of the K-means (25).
In addition, before performing further analysis, multivariate outliers were analysed by taking into consideration Mahalonobis distance.