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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.
Over dispersion is the phenomenon which occurs when empirical variance in the data exceeds the nominal variance under some supposed model. Most often encountered when the modeling
Component bar chart : A bar chart which shows the component parts of the aggregate represented by the whole length of the bar. The component parts are shown as the sectors of bar w
MAREG is the software package for the analysis of the marginal regression models. The package permits the application of generalized estimating equations and the maximum likelihoo
Conjoint analysis : The method used basically in market research which is similar in many respects to the various dimensional scaling. The method attempts to assign values to the l
5. Packages from a machine a normally distributed with a mean 200g and its standard deviation 2grams. Find the probability that a package from the machine weighs a) Less than
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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
Asymmetric proximity matrices : Proximity matrices in which the non-diagonal elements, in the ith row and jth column and the jth row and ith column, are not essentially equal. Exam
Lagrange Multiplier (LM) test The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1
Omitted covariates is a term generally found in the connection with regression modelling, where the model has been incompletely specified by not including significant covariates.
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