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Maximum likelihood estimation is an estimation procedure involving maximization of the likelihood or the log-likelihood with respect to the parameters. Such type of estimators is particularly important because of their many desirable statistical properties such as consistency, and asymptotic efficiency. As an example considers the number of successes, X, in a sequence of random variables from a Bernoulli distribution with success probability p. The likelihood can be given by differentiating the log-likelihood, L, with respect to p gives the following
Catastrophe theory : A theory of how little is the continuous changes in the independent variables which can have unexpected, discontinuous effects on the dependent variables. Exam
The approach to statistics based on a frequency view of probability in which it is supposed that it is possible to consider an in?nite sequence of the independent repetitions of th
Outliers - Reasons for Screening Data Outliers are due to data entry errors, subject is not a member of the population that the sample is trying to represent, or the subject i
Bioassay : It is an abbreviation of biological assay, which in its classical form includes an experiment conducted on biological material to determine relative potency of test and
Regression line drawn as y= c+ 1075x ,when x was2, and y was 239,given that y intercept was 11. Calculate the residual ?
The problematic and enigmatic theory of an inference introduced by the Fisher, which extracts a probability distribution for the parameter on the basis of the data without having f
Quantalassay: The experiment in which the groups of subjects are exposed to the different doses of, generally, a drug, to which the particular number respond. Data from such type
Kleiner Hartigan trees is a technique for displaying the multivariate data graphically as the 'trees' in which the values of the variables are coded into length of the terminal br
An analyst counted 17 A/B runs and 26 time series observations. Do these results suggest that the data are nonrandom? Explain
Observation-driven model is a term generally applied to models for the longitudinal data or time series which introduce within the unit correlation by specifying the conditional
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