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Full-Text Articles in Physical Sciences and Mathematics
Statistics For National Development, Sani I. Doguwa
Statistics For National Development, Sani I. Doguwa
CBN Journal of Applied Statistics (JAS)
Good statistics that has been collected according to agreed good practices are crucial as a tool for development. Gross domestic product (GDP) and other measures of economic activity such as Gross National Income (GNI) together with their individual components, show how the economy is responding to government policy and other influences. The balance of payments can demonstrate the requirement for policy adjustments and is also one of the indicators scrutinised by potential foreign investors in the country. Agricultural statistics clearly have implications for longer-term planning, particularly if they show a move away from the land into urban areas or a …
Big Macs And Eigenfactor Scores: Don't Let The Correlation Coefficients Fool You, Jevin D. West, Carl T. Bergstrom, Theodore C. Bergstrom
Big Macs And Eigenfactor Scores: Don't Let The Correlation Coefficients Fool You, Jevin D. West, Carl T. Bergstrom, Theodore C. Bergstrom
Ted C Bergstrom
A recent article by Phil Davis suggested that the Eigenvalue metric does adds little useful information to the more simply calculated measure of total citations published by the ISI. This paper argues that Davis's claim is an instance of a classic statistical fallacy of spurious correlation. Based on an analysis of the entire 2006 ISI Journal Citation Reports, we show that there are statistically and economically significant differences between the Eigenfactor metrics and the ISI's impact factor and total citations.
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 …