Abstract
Mean corrected higher order sample moments are asymptotically normally distributed. It is shown that both in the literature and popular software the estimates of their asymptotic covariance matrices are incorrect. An introduction to the infinitesimal jackknife is given and it is shown how to use it to correctly estimate the asymptotic covariance matrices of higher order sample moments. Another advantage in using the infinitesimal jackknife is the ease with which it may be used when stacking or sub-setting estimators. The estimates given are used to test the goodness of fit of a non-linear factor analysis model. A computationally accelerated form for infinitesimal jackknife estimates is given.
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Acknowledgments
The authors would like to thank the review team and in particular reviewer 2 who motivated the inclusion of the offline material. The research of the second author is supported by Grant EC02011-28875 from the Spanish Ministry of Science and Innovation.
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Appendix
Appendix
1.1 \(\nu _k\) has the form (2)
Note that for any two column vectors \(u\) and \(v\), \(u\otimes v\) is a permutation of \(v\otimes u\). Using this repeatedly if \(u_1,\ldots ,u_k\) is a permutation of \(v_1,\ldots ,v_k\)
for some permutation matrix \(P\).
Let
where the product is of order \(k\). This can be expanded as a sum of \(m\) terms of the form
where each \(u_j\) is either \(x\) or \(\mu \). This can be written in the form
where \(P\) is a permutation matrix, the Kronecker product is of order \(k\), and the sub-product of the \(x\)’s is of order \(i\).
This is a continuously differentiable function \(h_i\) of the expected values of \(x\) and \(x\otimes \cdots \otimes x\). Thus
where \(t_1(x)=x\) and for \(i>1\)
and the Kronecker product has \(i\) terms.
Let
Then \(h\) is continuously differentiable. Let
Using \(h\) and \(t\)
Thus
has the form (2).
1.2 Computing the Matrix \(C_k\)
Let \(a\in R^m\) and \(b\in R^n\). Then \(b\otimes a\) is a permutation of \(a\otimes b\) and
for some permutation matrix \(P_{mn}\). Note that
Thus
Let \(u_1,\ldots ,u_m\) be the coordinate vectors in \(R^m\) and \(v_1,\ldots ,v_n\) be the coordinate vectors in \(R^n\). Then the vectors \(u_i\otimes v_j\) with \(i=1,\ldots ,m\) and \(j=1,\ldots ,n\) are a basis for \(R^m\times R^n\). Order this basis lexicographically. Since
the columns of \(P_{mn}\) are \(v_j\otimes u_i\) in lexicographic order on \(i\) and \(j\). All of the components of \(v_j\otimes u_i\) are zero except for its \(r=(j-1)m+i\) component which is one. Thus the computation of \(P_{mn}\) is inexpensive.
In the notation of Section 4 note that the sum of all distinct permutations of
is equal to the sum on \(\ell =0,\ldots ,k-1\) of
Let
Then
is the sum of all distinct permutations of \((x_i-\bar{x})\otimes (x_j-\bar{x})\otimes \cdots \otimes (x_j-\bar{x})\) and the mean on \(j\) of these is
as asserted in Section 3.
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Jennrich, R., Satorra, A. The Infinitesimal Jackknife and Moment Structure Analysis Using Higher Order Moments. Psychometrika 81, 90–101 (2016). https://doi.org/10.1007/s11336-014-9426-9
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DOI: https://doi.org/10.1007/s11336-014-9426-9