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Articles 1 - 14 of 14
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 …
An Essay On The Nature And Significance Of Deception And Telling Lies, Sudhanshu K. Mishra
An Essay On The Nature And Significance Of Deception And Telling Lies, Sudhanshu K. Mishra
Sudhanshu K Mishra
A lie is an expression at deviance with the truth known or honestly believed by someone with an intention to deceive others for certain purpose, social or personal. An ability to lie might be evolutionary in nature possibly to help in survival, since it is found in the non-human world also. In the biological perspective, each individual is at war against all others. Thus viewed, lies are the cardinal virtues for survival and, by implication, the carriers of evolution. In the human world, lying is morally blameworthy in a relatively un-obscure way. There may be cases of lying to which …
Temporal Changes In The Parameters Of Statistical Distribution Of Journal Impact Factor, Sudhanshu K. Mishra
Temporal Changes In The Parameters Of Statistical Distribution Of Journal Impact Factor, Sudhanshu K. Mishra
Sudhanshu K Mishra
Statistical distribution of Journal Impact Factor (JIF) is characteristically asymmetric and non-mesokurtic. Even the distribution of log10(JIF) exhibits conspicuous skewness and non-mesokurticity. In this paper we estimate the parameters of Johnson SU distribution fitting to the log10(JIF) data for 10 years, 1999 through 2008, and study the temporal variations in those estimated parameters. We also study ‘over-the-samples stability’ in the estimated parameters for each year by the method of re-sampling close to bootstrapping. It has been found that log10(JIF) is Pearson-IV distributed. Johnson SU distribution fits very well to the data and yields parameters stable over the samples. We conclude …
Empirical Probability Distribution Of Journal Impact Factor And Over-The-Samples Stability In Its Estimated Parameters, Sudhanshu K. Mishra
Empirical Probability Distribution Of Journal Impact Factor And Over-The-Samples Stability In Its Estimated Parameters, Sudhanshu K. Mishra
Sudhanshu K Mishra
The data on JIFs provided by Thomson Scientific can only be considered as a sample since they do not cover the entire universe of those documents that cite an intellectual output (paper, article, etc) or are cited by others. Then, questions arise if the empirical distribution (best fit to the JIF data for any particular year) really represents the true or universal distribution, are its estimated parameters stable over the samples and do they have some scientific interpretation? It may be noted that if the estimated parameters do not exhibit stability over the samples (while the sample size is large …
A Note On Empirical Sample Distribution Of Journal Impact Factors In Major Discipline Groups, Sudhanshu K. Mishra
A Note On Empirical Sample Distribution Of Journal Impact Factors In Major Discipline Groups, Sudhanshu K. Mishra
Sudhanshu K Mishra
What type of statistical distribution do the Journal Impact Factors follow? In the past, researchers have hypothesized various types of statistical distributions underlying the generation mechanism of journal impact factors. These are: lognormal, normal, approximately normal, Weibull, negative exponential, combination of exponentials, Poisson, Generalized inverse Gaussian-Poisson, negative binomial, generalized Waring, gamma, etc. It is pertinent to note that the major characteristics of JIF data lay in the asymmetry and non-mesokurticity. The present study, frequently encounters Burr-XII, inverse Burr-III (Dagum), Johnson SU, and a few other distributions closely related to Burr distributions to best fit the JIF data in subject groups …
Does The Journal Impact Factor Help Make A Good Indicator Of Academic Performance?, Sudhanshu K. Mishra
Does The Journal Impact Factor Help Make A Good Indicator Of Academic Performance?, Sudhanshu K. Mishra
Sudhanshu K Mishra
Is journal impact factor a good measure of research merit? This question has assumed a great importance after the notification of the University Grants Commission (Minimum Qualifications for Appointment of Teachers and other Academic Staff in Universities and Colleges and Measures for the Maintenance of Standards in Higher Education) Regulations, 2009 on September 23rd 2009. Now publication of research papers/articles in reputed journals has become an important factor in assessment of the academic performance of teachers in colleges and universities in India. One of the measures of reputation and academic standard (rank or importance) of a journal is the so-called …
The Most Representative Composite Rank Ordering Of Multi-Attribute Objects By The Particle Swarm Optimization, Sudhanshu K. Mishra
The Most Representative Composite Rank Ordering Of Multi-Attribute Objects By The Particle Swarm Optimization, Sudhanshu K. Mishra
Sudhanshu K Mishra
Rank-ordering of individuals or objects on multiple criteria has many important practical applications. A reasonably representative composite rank ordering of multi-attribute objects/individuals or multi-dimensional points is often obtained by the Principal Component Analysis, although much inferior but computationally convenient methods also are frequently used. However, such rank ordering – even the one based on the Principal Component Analysis – may not be optimal. This has been demonstrated by several numerical examples. To solve this problem, the Ordinal Principal Component Analysis was suggested some time back. However, this approach cannot deal with various types of alternative schemes of rank ordering, mainly …
A Note On The Sub-Optimality Of Rank Ordering Of Objects On The Basis Of The Leading Principal Component Factor Scores, Sudhanshu K. Mishra
A Note On The Sub-Optimality Of Rank Ordering Of Objects On The Basis Of The Leading Principal Component Factor Scores, Sudhanshu K. Mishra
Sudhanshu K Mishra
This paper demonstrates that if we intend to optimally rank order n objects (candidates) each of which has m rank-ordered attributes or rank scores have been awarded by m evaluators, then the overall ordinal ranking of objects by the conventional principal component based factor scores turns out to be suboptimal. Three numerical examples have been provided to show that principal component based rankings do not necessarily maximize the sum of squared correlation coefficients between the individual m rank scores arrays, X(n,m), and overall rank scores array, Z(n).