Generally, instrumental variables estimators only have desirable asymptotic, not finite sample, properties, and inference is based on asymptotic approximations to the sampling distribution of the estimator.
42.
In invariant-theoretic conditional inference, the sampling distribution is conditioned on an invariant of the group of transformations ( with respect to which the Haar measure is defined ).
43.
Confidence intervals and hypothesis tests are two statistical procedures in which the quantiles of the sampling distribution of a particular statistic ( e . g . the standard score ) are required.
44.
If the estimator is derived from a sample statistic and is used to estimate some population statistic, then the expectation is with respect to the sampling distribution of the sample statistic.
45.
Using one of these sampling distributions, it is possible to compute either a one-tailed or two-tailed p-value for the null hypothesis that the coin is fair.
46.
Because Sobel s test evaluates a given sample on the normal distribution, small sample sizes and skewness of the sampling distribution can be problematic ( see Normal Distribution for more details ).
47.
The most simple-minded brute force approach would be to simply make a huge sample distribution of how the system has actually hopped between any two feature values over any time interval.
48.
When calculated from the same population, it has a different sampling distribution to that of the mean and is generally not normal ( but it may be close for large sample sizes ).
49.
Further inputs to the algorithm are the marginal sample distribution p ( x ) \, which has already been determined by the dominant eigenvector of P \, and the matrix valued Kullback Leibler distance function
50.
The bias depends both on the sampling distribution of the estimator and on the transform, and can be quite involved to calculate see unbiased estimation of standard deviation for a discussion in this case.
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