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The generalization of the normal distribution used for the characterization of functions. It is known as a Gaussian process because it has Gaussian distributed finite dimensional marginal distributions. A major attraction of the Gaussian processes is computational tractability. They are sometimes known as Gaussian random fields and are popular in the application of the nonparametric Bayesian models.
Ignorability : The missing data mechanism is said to be ignorable for likelihood inference if (1) the joint likelihood for the responses of the interest and missing data indicators
Locally weighted regression is the method of regression analysis in which the polynomials of degree one (linear) or two (quadratic) are used to approximate regression function in
Confounding: A procedure observed in some factorial designs in which it is impossible to differentiate between some main effects or interactions, on the basis of the particular d
Principal components regression analysis is a process often taken in use to overcome the problem of multicollinearity in the regression, when simply deleting a number of the expla
Misspecification is the term is applied to describe the assumed statistical models which are incorrect for one of the several of reasons, for instance, using the wrong probability
Kolmogorov Smirnov two-sample method is a distribution free technique which tests for any difference between the two populations probability distributions. The test is relied on t
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 term used in a variety of methods in statistics, but mostly to refer to the categorical variable, with a less number of levels, under examination in an experiment as a possible
methods of measuring trend
Kalman filter : A recursive procedure which gives an estimate of the signal when only the 'noisy signal' can be observed. The estimate is efficiently constructed by putting the exp
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