Robustness of a multivariate normal approximation for imputation of incomplete binary data

2007 ◽  
Vol 26 (6) ◽  
pp. 1368-1382 ◽  
Author(s):  
Coen A. Bernaards ◽  
Thomas R. Belin ◽  
Joseph L. Schafer
Keyword(s):  
Binary Data ◽  
Normal Approximation ◽  
Multivariate Normal ◽  
Multivariate Normal Approximation

scholarly journals Multivariate normal approximations by Stein's method and size bias couplings

1996 ◽  
Vol 33 (01) ◽  
pp. 1-17 ◽  
Author(s):  
Larry Goldstein ◽  
Yosef Rinott
Keyword(s):  
Normal Approximation ◽  
Stein’S Method ◽  
Dependence Structure ◽  
Stein's Method ◽  
Multivariate Normal ◽  
Nonlinear Functions ◽  
Normal Approximations ◽  
Multivariate Normal Approximation ◽  
Non Local ◽  
Size Bias

Stein's method is used to obtain two theorems on multivariate normal approximation. Our main theorem, Theorem 1.2, provides a bound on the distance to normality for any non-negative random vector. Theorem 1.2 requires multivariate size bias coupling, which we discuss in studying the approximation of distributions of sums of dependent random vectors. In the univariate case, we briefly illustrate this approach for certain sums of nonlinear functions of multivariate normal variables. As a second illustration, we show that the multivariate distribution counting the number of vertices with given degrees in certain random graphs is asymptotically multivariate normal and obtain a bound on the rate of convergence. Both examples demonstrate that this approach may be suitable for situations involving non-local dependence. We also present Theorem 1.4 for sums of vectors having a local type of dependence. We apply this theorem to obtain a multivariate normal approximation for the distribution of the random p-vector, which counts the number of edges in a fixed graph both of whose vertices have the same given color when each vertex is colored by one of p colors independently. All normal approximation results presented here do not require an ordering of the summands related to the dependence structure. This is in contrast to hypotheses of classical central limit theorems and examples, which involve for example, martingale, Markov chain or various mixing assumptions.

scholarly journals Multivariate normal approximations by Stein's method and size bias couplings

1996 ◽  
Vol 33 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Larry Goldstein ◽  
Yosef Rinott
Keyword(s):  
Normal Approximation ◽  
Stein’S Method ◽  
Dependence Structure ◽  
Stein's Method ◽  
Multivariate Normal ◽  
Nonlinear Functions ◽  
Normal Approximations ◽  
Multivariate Normal Approximation ◽  
Non Local ◽  
Size Bias

Stein's method is used to obtain two theorems on multivariate normal approximation. Our main theorem, Theorem 1.2, provides a bound on the distance to normality for any non-negative random vector. Theorem 1.2 requires multivariate size bias coupling, which we discuss in studying the approximation of distributions of sums of dependent random vectors. In the univariate case, we briefly illustrate this approach for certain sums of nonlinear functions of multivariate normal variables. As a second illustration, we show that the multivariate distribution counting the number of vertices with given degrees in certain random graphs is asymptotically multivariate normal and obtain a bound on the rate of convergence. Both examples demonstrate that this approach may be suitable for situations involving non-local dependence. We also present Theorem 1.4 for sums of vectors having a local type of dependence. We apply this theorem to obtain a multivariate normal approximation for the distribution of the random p-vector, which counts the number of edges in a fixed graph both of whose vertices have the same given color when each vertex is colored by one of p colors independently. All normal approximation results presented here do not require an ordering of the summands related to the dependence structure. This is in contrast to hypotheses of classical central limit theorems and examples, which involve for example, martingale, Markov chain or various mixing assumptions.

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