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High-dimensional data: This term used for data sets which are characterized by the very large number of variables and a much more modest number of the observations. In the 21st century\ such data sets are collected in number of areas, such as, text/web data mining and bioinformatics. The job of extracting meaningful statistical and biological information from such data sets present many challenges for which a number of recent methodological developments, for instance, sure screening methods, lasso, and Dantzig selector, might be quite helpful.
properties of chebyshevs lemma
Mendelian randomization is the term applied to the random assortment of alleles at the time of gamete formation, a process which results in the population distributions of genetic
Pattern recognition is a term for a technology that recognizes and analyses patterns automatically by machine and which has been used successfully in many areas of application inc
Data which occur when failure period is recorded which are dependent. Such type of data can arise in number contexts, for instance, in epidemiological cohort studies in which th
Non linear mapping (NLM ) is a technique for obtaining a low-dimensional representation of the set of multivariate data, which operates by minimizing a function of the differences
Residual plots are the plots of some type of residual which might be helpful in assessing the assumption made by the fitted model. In regression analysis there are various method
methods of measuring trend
An oil company thinks that there is a 60% chance that there is oil in the land they own. Before drilling they run a soil test. When there is oil in the ground, the soil test comes
Bayesian confidence interval : An interval of the posterior distribution which is so that the density of it at any point inside the interval is greater than that of the density at
The process of providing the numerical value for the population parameter on the basis of information gathered from a sample. If a single ?gure is computed for the unknown paramete
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