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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.
Point scoring is an easy distribution free method which can be used for the prediction of a response which is a binary variable from the observations on several explanatory variab
Ordination is the procedure of reducing the dimensionality (that is the number of variables) of multivariate data by deriving the small number of new variables which contain much
elements , importance, limitation, and theories
#explanation of methods of collection of data..
Minimum volume ellipsoid is a term for ellipsoid of the minimum volume which covers some specified proportion of the set of multivariate data. It is commonly used to construct rob
The Null Hypothesis - H0: γ 1 = γ 2 = ... = 0 i.e. there is no heteroscedasticity in the model The Alternative Hypothesis - H1: at least one of the γ i 's are not equal
Human height growth curves : The growth of human height is, in common, remarkably regular, apart from the pubertal growth spurt. The satisfactory longitudinal development curve is
Calibration : A procedure which enables a series of simply obtainable but inaccurate measurements of some quantity of interest to be used to provide more precise estimates of the r
Lexis diagram is the diagram for displaying the simultaneous effects of the two time scales (generally age and calendar time) on a rate. For instance, mortality rates from cancer
Reasons for screening data Garbage in-garbage out Missing data a. Amount of missing data is less crucial than the pattern of it. If randomly
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