Statistics

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

In this paper we study a periodic review inventory model with stock dependent demand. When stock on hand is zero, the inventory manager offers a price discount to customers who are willing to backorder their demand. Permissible delay in payments allowed to the inventory manager is also taken into account. Numerical examples are cited to illustrate the model.

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Yugoslav Journal of Operations Research

A periodic review inventory model with stock dependent demand, permissible delay in payment and price discount on backorders

Theorem 2.2 of Majumdar (Sankhyā 67:670-673, 2005) obtained two conditions that are necessary and jointly sufficient for weak convergence in M(H). In this note we significantly relax the second condition while maintaining the joint sufficiency. © 2013, Indian Statistical Institute.

Sankhya: The Indian Journal of Statistics

On weak convergence in M(H): Relaxation of a sufficient condition

Theorem 2.2 of Majumdar (Sankhyā 67:670–673, 2005) obtained two conditions that are necessary and jointly sufficient for weak convergence in M(H). In this note we significantly relax the second condition while maintaining the joint sufficiency. © 2013, Indian Statistical Institute.

Sankhya A

On weak convergence in M(H): relaxation of a sufficient condition

Scientific formalizations of the notion of growth and measurement of the rate of growth in living organisms are age-old problems. The most frequently used metric, "Average Relative Growth Rate" is invariant under the choice of the underlying growth model. Theoretically, the estimated rate parameter and relative growth rate remain constant for all mutually exclusive and exhaustive time intervals if the underlying law is exponential but not for other common growth laws (e.g., logistic, Gompertz, power, general logistic). We propose a new growth metric specific to a particular growth law and show that it is capable of identifying the underlying growth model. The metric remains constant over different time intervals if the underlying law is true, while the extent of its variation reflects the departure of the assumed model from the true one. We propose a new estimator of the relative growth rate, which is more sensitive to the true underlying model than the existing one. The advantage of using this is that it can detect crucial intervals where the growth process is erratic and unusual. It may help experimental scientists to study more closely the effect of the parameters responsible for the growth of the organism/population under study. © 2014 Springer Science+Business Media Dordrecht.

Journal of Biological Physics

Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: A new approach

In this chapter, we present standard mixture models and standard mixture designs as generally applied to such models. These mixture models and mixture designs will occur throughout the monograph. Several generalizations of standard mixture designs are also discussed here. Estimability issues involving the model parameters are addressed at length. In the process, information matrices are worked out and their roles are emphasized. The concept of Loewner domination is also brought in. © 2014, Springer India.

GENERAL

Lecture Notes in Statistics

Parameter estimation in linear and quadratic mixture models

Astronomy in recent past has developed a lot with the launch of several missions like GALEX (Galaxy Evolution Explorer), Kepler Space Telescope, Hubble Space Telescope (HST), etc. through which terabytes of data are available for preservation. This increasing proportionality of huge data demands data access efficiency. The implication of the above statement is important in a sense that most of the astrophysical phenomena are being observed in terms of light intensity as a function of wavelength or frequency which are snapshots of experiments which cannot be repeated as such. So one can easily understand why every single observation needs to be preserved. Thus, on the one hand, it requires the advent of more sophisticated observing instruments and sophisticated data management systems are to be developed, on the other hand, to complement the above challenge. With the above point in view astronomers have developed several Virtual data archives like SDSS (Sloan digital sky survey), MAST (Multi mission archive at STSCI), EXOSAT (European X-ray observatory Satellite), CGRO (Compton Gamma Ray Observatory), GALEX, Vizier, EDD, LEDA, HETE-2 (High Energy Transient Explorer), Chandra, Swift, ROSAT (Rontgen Satellite), WMAP (Wilkinson Microwave Anisotropy Probe), NED (NASA Extragalactic Data base), PDSC (Planetary Data System) etc. and in the making of future missions like ALMA (Atacama Large Millimeter Array), SKA (Square Kilometer Array) in 2025, etc. © Springer Science+Business Media New York 2014.

Applied Mathematics

Springer Series in Astrostatistics

Sources of astronomical data

Cluster analysis is an art of finding groups in variables or observations. Over the last 50 years different algorithms and computer programs have been developed for cluster analysis. Generally clustering algorithms can be divided into two principal types, viz. partitioning and hierarchical methods. © Springer Science+Business Media New York 2014.

Clustering, classification and data mining

Statistical analysis with missing data is an important problem as the problem of missing observation is very common in many situations. During the last two decades different methods have been developed to tackle the situation. One possible way to handle missing values is to remove either all features or all objects that contain missing values. Another possibility is imputation where we fill in the missing values by inferring new values for them. The imputation method may not be applicable to some astronomical data sets as the missing value may arise from physical process and imputing missing values is misleading and can skew subsequent analysis of data. For example, the Lyman break technique (Giavalisco 2002) can identify high-redshift galaxies based on the absence of detectable emissions in bands corresponding to the FUV rest frame of the objects. © Springer Science+Business Media New York 2014.

Missing observations and imputation

Population: In any Statistical Analysis we are interested in some numerical characteristic of an aggregate of individuals rather than in the characteristics of the individuals themselves. Such an aggregate is called a “population” or “universe”. © Springer Science+Business Media New York 2014.

Statistical inference

This chapter addresses the problem of finding optimum designs for the estimation of optimum mixture combination when the mean response is defined by (i) the additive quadratic mixture model due to Darroch and Waller (1985) and (ii) the quadratic log-contrast model due to Aichison and Bacon-Shoane (1984). Both the models have some advantage over Scheffé quadratic mixture model, in specific situations. © 2014, Springer India.