Some Comments on Egghe’s Derivation of the Impact Factor Distribution

In a recent paper, Egghe [Egghe, L. (in press). Mathematical derivation of the impact factor distribution. Journal of Informetrics] provides a mathematical analysis of the rank-order distribution of journal impact factors. We point out that Egghe’s analysis relies on an unrealistic assumption, and we show that his analysis is not in agreement with empirical data.distribution;rank-order distribution;impact factor

Characterizing Ranked Chinese Syllable-to-Character Mapping Spectrum: A Bridge Between the Spoken and Written Chinese Language

One important aspect of the relationship between spoken and written Chinese is the ranked syllable-to-character mapping spectrum, which is the ranked list of syllables by the number of characters that map to the syllable. Previously, this spectrum is analyzed for more than 400 syllables without distinguishing the four intonations. In the current study, the spectrum with 1280 toned syllables is analyzed by logarithmic function, Beta rank function, and piecewise logarithmic function. Out of the three fitting functions, the two-piece logarithmic function fits the data the best, both by the smallest sum of squared errors (SSE) and by the lowest Akaike information criterion (AIC) value. The Beta rank function is the close second. By sampling from a Poisson distribution whose parameter value is chosen from the observed data, we empirically estimate the $p$-value for testing the two-piece-logarithmic-function being better than the Beta rank function hypothesis, to be 0.16. For practical purposes, the piecewise logarithmic function and the Beta rank function can be considered a tie.Comment: 15 pages, 4 figure

Fitting Ranked English and Spanish Letter Frequency Distribution in U.S. and Mexican Presidential Speeches

The limited range in its abscissa of ranked letter frequency distributions causes multiple functions to fit the observed distribution reasonably well. In order to critically compare various functions, we apply the statistical model selections on ten functions, using the texts of U.S. and Mexican presidential speeches in the last 1-2 centuries. Dispite minor switching of ranking order of certain letters during the temporal evolution for both datasets, the letter usage is generally stable. The best fitting function, judged by either least-square-error or by AIC/BIC model selection, is the Cocho/Beta function. We also use a novel method to discover clusters of letters by their observed-over-expected frequency ratios.Comment: 7 figure

Diminishing Return for Increased Mappability with Longer Sequencing Reads: Implications of the k-mer Distributions in the Human Genome

The amount of non-unique sequence (non-singletons) in a genome directly affects the difficulty of read alignment to a reference assembly for high throughput-sequencing data. Although a greater length increases the chance for reads being uniquely mapped to the reference genome, a quantitative analysis of the influence of read lengths on mappability has been lacking. To address this question, we evaluate the k-mer distribution of the human reference genome. The k-mer frequency is determined for k ranging from 20 to 1000 basepairs. We use the proportion of non-singleton k-mers to evaluate the mappability of reads for a corresponding read length. We observe that the proportion of non-singletons decreases slowly with increasing k, and can be fitted by piecewise power-law functions with different exponents at different k ranges. A faster decay at smaller values for k indicates more limited gains for read lengths > 200 basepairs. The frequency distributions of k-mers exhibit long tails in a power-law-like trend, and rank frequency plots exhibit a concave Zipf's curve. The location of the most frequent 1000-mers comprises 172 kilobase-ranged regions, including four large stretches on chromosomes 1 and X, containing genes with biomedical implications. Even the read length 1000 would be insufficient to reliably sequence these specific regions.Comment: 5 figure

Analyses of Baby Name Popularity Distribution in U.S. for the Last 131 Years

We examine the complete dataset of baby name popularity collected by U.S. Social Security Administration for the last 131 years (1880-2010). The ranked baby name popularity can be fitted empirically by a piecewise function consisting of Beta function for the high-ranking names and power-law function for low-ranking names, but not power-law (Zipf's law) or Beta function by itself.Comment: 6 figure