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| Bulla, Ingo; Aliaga, Benoit; Lacal, Virginia; Bulla, Jan; Grunau, Christoph; Chaparro, Cristian. |
| Background: DNA methylation patterns store epigenetic information in the vast majority of eukaryotic species. The relatively high costs and technical challenges associated with the detection of DNA methylation however have created a bias in the number of methylation studies towards model organisms. Consequently, it remains challenging to infer kingdom-wide general rules about the functions and evolutionary conservation of DNA methylation. Methylated cytosine is often found in specific CpN dinucleotides, and the frequency distributions of, for instance, CpG observed/expected (CpG o/e) ratios have been used to infer DNA methylation types based on higher mutability of methylated CpG. Results: Predominantly model-based approaches essentially founded on... |
| Tipo: Text |
Palavras-chave: Epigenetics; DNA methylation; Kernel density estimation; CpG o/e ratio; CpN o/e ratio. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00437/54816/71790.pdf |
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| Salgado-Ugarte, Isaias H.; Perez-Hernandez, Marco A.. |
| Variable bandwidth kernel density estimators increase the window width at low densities and decrease it where data concentrate. This represents an improvement over the fixed bandwidth kernel density estimators. In this article, we explore the use of one implementation of a variable kernel estimator in conjunction with several rules and procedures for bandwidth selection applied to several real datasets. The considered examples permit us to state that when working with tens or a few hundreds of data observations, least-squares cross-validation bandwidth rarely produces useful estimates; with thousands of observations, this problem can be surpassed. Optimal bandwidth and biased cross-validation (BCV), in general, oversmooth multimodal densities. The... |
| Tipo: Journal Article |
Palavras-chave: Kernel density estimation; Bandwidth; Cross validation; Multimodality test; Research Methods/ Statistical Methods. |
| Ano: 2003 |
URL: http://purl.umn.edu/116063 |
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| Cox, Nicholas J.. |
| Density probability plots show two guesses at the density function of a continuous variable, given a data sample. The first guess is the density function of a specified distribution (e.g., normal, exponential, gamma, etc.) with appropriate parameter values plugged in. The second guess is the same density function evaluated at quantiles corresponding to plotting positions associated with the sample’s order statistics. If the specified distribution fits well, the two guesses will be close. Such plots, suggested by Jones and Daly in 1995, are explained and discussed with examples from simulated and real data. Comparisons are made with histograms, kernel density estimation, and quantile–quantile plots. |
| Tipo: Journal Article |
Palavras-chave: Density probability plots; Distributions; Histograms; Kernel density estimation; Quantile–quantile plots; Statistical graphics; Research Methods/ Statistical Methods. |
| Ano: 2005 |
URL: http://purl.umn.edu/117517 |
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