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The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged [3] even though the RDD API is not deprecated. [4] [5] The RDD technology still underlies the Dataset API. [6] [7]
The estimator can be derived in terms of the generalized method of moments (GMM). Also often discussed in the literature (including White's paper) is the covariance matrix Ω ^ n {\displaystyle {\widehat {\mathbf {\Omega } }}_{n}} of the n {\displaystyle {\sqrt {n}}} -consistent limiting distribution:
After calculating the column prior probabilities the alignment probability is estimated by summing over all possible secondary structures. Any column C in a secondary structure σ {\displaystyle \sigma } for a sequence D of length l such that D = ( C 1 , C 2 , . . .
A Newey–West estimator is used in statistics and econometrics to provide an estimate of the covariance matrix of the parameters of a regression-type model where the standard assumptions of regression analysis do not apply. [1] It was devised by Whitney K. Newey and Kenneth D. West in 1987, although there are a number of later variants.
The convergence of a regular estimator's distribution is, in a sense, locally uniform. This is often considered desirable and leads to the convenient property that a small change in the parameter does not dramatically change the distribution of the estimator.
, X n) be an estimator based on a random sample X 1,X 2, . . . , X n, the estimator T is called an unbiased estimator for the parameter θ if E[T] = θ, irrespective of the value of θ. [1] For example, from the same random sample we have E(x̄) = μ (mean) and E(s 2) = σ 2 (variance), then x̄ and s 2 would be unbiased estimators for μ and ...
Clearly, the difference between the unbiased estimator and the maximum likelihood estimator diminishes for large n. In the general case, the unbiased estimate of the covariance matrix provides an acceptable estimate when the data vectors in the observed data set are all complete: that is they contain no missing elements. One approach to ...
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.