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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.
Response feature analysis is the approach to the analysis of longitudinal data including the calculation of the suitable summary measures from the set of repeated measures on each
Explain the impact of globalisationon HRM
Different approaches to the study of early indian history
Q. A toothpaste company want to know if its new product increases the length of time in-between dentist visit to its user. The company sets a target for 180 days to determine if it
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
Likert scales is often used in the studies of attitudes in which the raw scores are based on the graded alternative responses to each of a series of queries. For instance, the sub
Mann Whitney test is a distribution free test which is used as an alternative to the Student's t-test for assessing that whether the two populations have the same median. The test
Group visible design is an arrangement of the v mn treatments in b blocks such that: * Each block comprises k distinct treatments k5v; * Each treatment is replicated r number
Lattice distribution : A class of probability distributions to which most of the distributions for discrete random variables used in statistics belongs. In such type of distributio
Please help with following problem: : Let’s consider the logistic regression model, which we will refer to as Model 1, given by log(pi / [1-pi]) = 0.25 + 0.32*X1 + 0.70*X2 + 0.
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