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In later years, Nida distanced himself from the term "dynamic equivalence" and preferred the term "functional equivalence". [ 2 ] [ 3 ] [ 4 ] What the term "functional equivalence" suggests is not just that the equivalence is between the function of the source text in the source culture and the function of the target text (translation) in the ...
Equivalence tests are a variety of hypothesis tests used to draw statistical inferences from observed data. In these tests, the null hypothesis is defined as an effect large enough to be deemed interesting, specified by an equivalence bound. The alternative hypothesis is any effect that is less extreme than said equivalence bound.
The function h(V) is effectively the control function that models the endogeneity and where this econometric approach lends its name from. [4]In a Rubin causal model potential outcomes framework, where Y 1 is the outcome variable of people for who the participation indicator D equals 1, the control function approach leads to the following model
In the case where the maximization is an integral of a concave function of utility over an horizon (0,T), dynamic programming is used. There is no certainty equivalence as in the older literature, because the coefficients of the control variables—that is, the returns received by the chosen shares of assets—are stochastic.
The significant advantage of the variational approaches is that the meteorological fields satisfy the dynamical equations of the NWP model and at the same time they minimize the functional, characterizing their difference from observations. Thus, the problem of constrained minimization is solved.
In statistics, an F-test of equality of variances is a test for the null hypothesis that two normal populations have the same variance.Notionally, any F-test can be regarded as a comparison of two variances, but the specific case being discussed in this article is that of two populations, where the test statistic used is the ratio of two sample variances. [1]
The equivalence between conserved quantities and corresponding symmetry groups implies a similar equivalence for these two ways of specifying the testable information in the maximum entropy method. The maximum entropy principle is also needed to guarantee the uniqueness and consistency of probability assignments obtained by different methods ...
In statistics, a location parameter of a probability distribution is a scalar- or vector-valued parameter, which determines the "location" or shift of the distribution.In the literature of location parameter estimation, the probability distributions with such parameter are found to be formally defined in one of the following equivalent ways: