منابع مشابه
Maximal conditional chi-square importance in random forests
MOTIVATION High-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. W...
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Probability, Random Chance, and Genetics Why do we study random chance and probability during a unit on genetics? Genetics is the study of inheritance, but it is also a study of probability. Most eukaryotic organisms are diploid, meaning that each cell contains two copies of every chromosome, so there are two copies of each gene that controls a trait (alleles). In sexual reproduction, these two...
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The information inequality has been shown to be an effective tool for providing lower bounds for the minimax risk. Bounds based on the chisquare distance can sometimes offer a considerable improvement especially when applied iteratively. This paper compares these two methods in four examples including the bounded normal mean problem as well as obervations from a Poisson distribution.
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The Chi square test is a statistical test which measures the association between two categorical variables. A working knowledge of tests of this nature are important for the chiropractor and osteopath in order to be able to critically appraise the literature.
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Chi-square tests are generally used for distinguishing purposes; however when they are combined to simultaneously test several independent variables, extra notation is required. In this study, the chi-square statistics in some previous works is revealed to be computed half of its real value. Therefore, the notion of Multi _ Chi-square tests is formulated to avoid possible future confusions. In ...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2010
ISSN: 1460-2059,1367-4803
DOI: 10.1093/bioinformatics/btq038