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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Disparities In Globalization Of The World Economies, Sudhanshu K. Mishra, Binod Kumar
Disparities In Globalization Of The World Economies, Sudhanshu K. Mishra, Binod Kumar
Sudhanshu K Mishra
This paper constructs composite indices of globalization of 131 countries spread over the five continents and classified into World-I, World-II and World-III countries. KOF, the Business Cycle Research Institute in the Swiss Federal Institute of Technology, Zurich is the source of data used in this study. The Composite Indices of Globalization have been computed by Pena’s method, which attributes the most desirable properties to the indices so constructed. On the basis of these indices, the paper investigates into the trends of globalization and disparities in globalization for a period of 11 years (1999-2009). Disparities have been obtained as the Gini’s …
Global Optimization Of Some Difficult Benchmark Functions By Cuckoo-Host Co-Evolution Meta-Heuristics, Sudhanshu K. Mishra
Global Optimization Of Some Difficult Benchmark Functions By Cuckoo-Host Co-Evolution Meta-Heuristics, Sudhanshu K. Mishra
Sudhanshu K Mishra
This paper proposes a novel method of global optimization based on cuckoo-host co-evaluation. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 96 benchmark functions (of which the results of 32 relatively harder problems have been reported). The proposed method is comparable to the Differential Evolution method of global optimization.
Construction Of Pena’S Dp2-Based Ordinal Synthetic Indicator When Partial Indicators Are Rank Scores, Sudhanshu K. Mishra
Construction Of Pena’S Dp2-Based Ordinal Synthetic Indicator When Partial Indicators Are Rank Scores, Sudhanshu K. Mishra
Sudhanshu K Mishra
The present study devises a computational scheme (and develops a FORTRAN 77 computer program) that may be appropriate to construct Pena’s DP2 (ordinal) synthetic indicator (Z) from the partial indicators (X) all of which are ordinal (ranking scores). An attempt has also been made to empirically apply the method (and the computer program) to obtain an ordinal synthetic indicator from a given ordinal data set.
A Comparative Study Of Trends In Globalization Using Different Synthetic Indicators, Sudhanshu K. Mishra
A Comparative Study Of Trends In Globalization Using Different Synthetic Indicators, Sudhanshu K. Mishra
Sudhanshu K Mishra
Using the KOF data at the annual level, we construct ten different composite indices for comparing the extent of globalization of 131 countries for eleven years, 1999-2009. We compare the different indices of globalization among themselves and also with the Dreher-KOF index of globalization and find that among the different indices the Dreher-Chebyshev index is the most representative one. Among the countries, we concentrate on the trends in globalization of India and her neighboring countries, Bangladesh, China, and Pakistan.
A Maximum Entropy Perspective Of Pena’S Synthetic Indicators, Sudhanshu K. Mishra
A Maximum Entropy Perspective Of Pena’S Synthetic Indicators, Sudhanshu K. Mishra
Sudhanshu K Mishra
This paper uses mixed combinatorial-cum-real particle swarm method to obtain a heuristically optimal order in which the constituent variables can be arranged so as to yield some generalized maximum entropy synthetic indicators that represent the constituent variables in the best information-theoretic sense. It may help resolve the arbitrariness and indeterminacy of Pena’s method of construction of a synthetic indicator which at present is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged.
A Note On Construction Of Heuristically Optimal Pena’S Synthetic Indicators By The Particle Swarm Method Of Global Optimization, Sudhanshu K. Mishra
A Note On Construction Of Heuristically Optimal Pena’S Synthetic Indicators By The Particle Swarm Method Of Global Optimization, Sudhanshu K. Mishra
Sudhanshu K Mishra
Pena’s method of construction of a synthetic indicator is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged. Due to this, Pena’s method can at present give only an arbitrary synthetic indicator whose representativeness is indeterminate and uncertain, especially when the number of constituent variables is not very small. This paper uses discrete global optimization method based on the Particle Swarms to obtain a heuristically optimal order in which the constituent variables can be arranged so as to yield Pena’s synthetic indicator that maximizes the minimal absolute (or squared) correlation …
A Note On The Indeterminacy And Arbitrariness Of Pena’S Method Of Construction Of Synthetic Indicators, Sudhanshu K. Mishra
A Note On The Indeterminacy And Arbitrariness Of Pena’S Method Of Construction Of Synthetic Indicators, Sudhanshu K. Mishra
Sudhanshu K Mishra
In this paper we demonstrate that Pena’s method of construction of a synthetic indicator is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged. Since m number of constituent variables may be arranged in m-factorial ways, even a moderately large m can give rise to a very large number of synthetic indicators from which one cannot choose the one which best represents the constituent variables. Given that an analyst has too little information as to the order in which a sizeable number of constituent variables must be arranged so as …