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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.
Log-linear models is the models for count data in which the logarithm of expected value of a count variable is modelled as the linear function of parameters; the latter represent
Remedian: The robust estimator of location which is computed by an iterative process. By assuming that the sample size n can be written as bk where b and k are the integers, the s
Human capital model : The model for evaluating the economic implication of the disease in terms of the economic loss of a person succumbing to morbidity or the mortality at some pa
Pasture trials is the study in which the pastures are subjected to number of treatments (types of forage, animal management systems, agronomic treatments, and many more)The grazin
A term which covers the large number of techniques for the analysis of the multivariate data which have in common the aim to assess whether or not the set of variables distinguish
Isobologram is a diagram used to characterize the interactions among jointly administered drugs or the chemicals. The contour of the constant response (that is the isobole), which
wat iz z difference b/n logistic regression and multiple regression analysis /
Cluster randomization : The random allocation of the groups or clusters of the individuals in the formation of treatment groups.Eeven though not as statistically ef?cient as the in
The computer programs designed to mimic the role of the expert human consultant. This type of systems are capable to cope with the complex problems of the medical decision makin
The Expectation/Conditional Maximization Either algorithm which is the generalization of ECM algorithm attained by replacing some of the CM-steps of ECM which maximize the constrai
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