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Full-Text Articles in Statistics and Probability
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 …
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 …