Skip to main content

Prasanta Chandra Mahalanobis

Prasanta Chandra Mahalanobis :
                                                  



Born
29 June 1893
Calcutta, Bengal, British India
Died
28 June 1972 (aged 78)
Calcutta, West Bengal, India
Residence
India, United Kingdom, United States
Nationality
Fields
Institutions
Known for
Notable awards



Indian Statistical Institute:


Many colleagues of Mahalanobis took an interest in statistics and the group grew in the Statistical Laboratory located in his room at the Presidency College, Calcutta. A meeting was called on the 17 December 1931 with Pramatha Nath Banerji (Minto Professor of Economics), Nikhil Ranjan Sen (Khaira Professor of Applied Mathematics) and Sir R. N. Mukherji. The meeting led to the establishment of the Indian Statistical Institute (ISI), and formally registered on 28 April 1932 as a non-profit distributing learned society under the Societies Registration Act XXI of 1860.[1]
The Institute was initially in the Physics Department of the Presidency College and the expenditure in the first year was Rs. 238. It gradually grew with the pioneering work of a group of his colleagues including S. S. Bose, J. M. Sengupta, R. C. Bose, S. N. Roy, K. R. Nair, R. R. Bahadur, Gopinath Kallianpur, D. B. Lahiri and C. R. Rao. The institute also gained major assistance through Pitamber Pant, who was a secretary to the Prime Minister Jawaharlal Nehru. Pant was trained in statistics at the Institute and took a keen interest in the institute.[1]
In 1933, the journal Sankhya was founded along the lines of Karl Pearson's Biometrika.[1]
The institute started a training section in 1938. Many of the early workers left the ISI for careers in the United States and with the government of India. Mahalanobis invited J. B. S. Haldane to join him at the ISI and Haldane joined as a Research Professor from August 1957 and stayed on until February 1961. He resigned from the ISI due to frustrations with the administration and disagreements with Mahalanobis's policies. He was also very concerned with the frequent travels and absence of the director and wrote The journeyings of our Director define a novel random vector. Haldane however helped the ISI grow in biometrics.[3]
In 1959, the institute was declared as an institute of national importance and a deemed university.[1]



Mahalanobis five year plan:


The model was created as an analytical framework for India’s Second Five Year Plan in 1955 by appointment of Prime Minister Jawaharlal Nehru, as India felt there was a need to introduce a formal plan model after the First Five Year Plan (1951-1956). The First Five Year Plan stressed investment for capital accumulation in the spirit of the one-sector Harrod–Domar model. It argued that production required capital and that capital can be accumulated through investment; the faster one accumulates, the higher the growth rate will be. The most fundamental criticisms came from Mahalanobis, who himself was working with a variant of it in 1951 and 1952. The criticisms were mostly around the model’s inability to cope with the real constraints of the economy; it’s ignoring of the fundamental choice problems of planning over time; and the lack of connection between the model and the actual selection of projects for governmental expenditure. Subsequently Mahalanobis introduced his celebrated two-sector model, which he later expanded into a four-sector version. 

 

Comments

Popular posts from this blog

Frequency Polygons

Learning Objectives Create and interpret frequency polygons Create and interpret cumulative frequency polygons Create and interpret overlaid frequency polygons Frequency polygons are a graphical device for understanding the shapes of distributions. They serve the same purpose as histograms, but are especially helpful for comparing sets of data. Frequency polygons are also a good choice for displaying cumulative frequency distributions . To create a frequency polygon, start just as for histograms , by choosing a class interval. Then draw an X-axis representing the values of the scores in your data. Mark the middle of each class interval with a tick mark, and label it with the middle value represented by the class. Draw the Y-axis to indicate the frequency of each class. Place a point in the ...

Lognormal distribution

Lognormal Distribution Probability Density Function A variable X is lognormally distributed if Y = LN(X) is normally distributed with "LN" denoting the natural logarithm. The general formula for the  probability density function  of the lognormal distribution is where   is the  shape parameter ,   is the  location parameter  and  m is the  scale parameter . The case where   = 0 and  m  = 1 is called the  standard lognormal distribution . The case where   equals zero is called the 2-parameter lognormal distribution. The equation for the standard lognormal distribution is Since the general form of probability functions can be  expressed in terms of the standard distribution , all subsequent formulas in this section are given for the standard form of the function. The following is the plot of the lognormal probability density function for four values of  . There are several commo...

Basics of Sampling Techniques

Population                A   population   is a group of individuals(or)aggregate of objects under study.It is also known as universe. The population is divided by (i)finite population  (ii)infinite population, (iii) hypothetical population,  subject to a statistical study . A population includes each element from the set of observations that can be made. (i) Finite population : A population is called finite if it is possible to count its individuals. It may also be called a countable population. The number of vehicles crossing a bridge every day, (ii) Infinite population : Sometimes it is not possible to count the units contained in the population. Such a population is called infinite or uncountable. ex, The number of germs in the body of a patient of malaria is perhaps something which is uncountable   (iii) Hypothetical population : Statistica...