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Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional quantile functions. An instance of such a model is the least absolute deviation regression. Quantile regression is generally capable of giving a more complete statistical analysis of the stochastic relationship amongs random variables.
R-squared is regarded as the coefficient of determination and is used to give the proportion of the fluctuation of the variance of one variable to another variable. R-squared also
What is a Generalized Linear Model? A traditional linear model is of the form where Yi is the response variable for the ith observation, xi is a column vector of explanator
Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric max
Bonferroni correction : A procedure for guarding against the rise in the probability of a type I error when performing the multiple signi?cance tests. To maintain probability of a
Goodmanand kruskal measures of association is the measures of associations which are useful in the situation where two categorical variables cannot be supposed to be derived from
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
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
Yate s' continuity correction : When the testing for independence in contingency table, a continuous probability distribution, known as chi-squared distribution, is used as the app
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The nonparametric Bayesian inference approach to using the finite mixture distributions for modelling data suspected of the containing distinct groups of observations; this approac
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