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Duchesne, T., Rioux, J. and Luong, A. (1997) Minimum Cramér-Von Mises Distance Methods for Complete and Grouped Data. Communications in Statistics, Theory and Methods, 26, 401-420.
https://doi.org/10.1080/03610929708831923

has been cited by the following article:

• TITLE: Simulated Minimum Quadratic Distance Methods Using Grouped Data for Some Bivariate Continuous Models

  AUTHORS: Andrew Luong

  KEYWORDS: Projected Score Functions, Adaptive Basis, Complete Basis, Chi-Square Tests Statistics, Random Cells, Contingency Table

  JOURNAL NAME: Open Journal of Statistics, Vol.8 No.2, April 30, 2018

  ABSTRACT: Quadratic distance methods based on a special distance which make use of survival functions are developed for inferences for bivariate continuous models using selected points on the nonegative quadrant. A related version which can be viewed as a simulated version is also developed and appears to be suitable for bivariate distributions with no closed form expressions and numerically not tractable but it is easy to simulate from these distributions. The notion of an adaptive basis is introduced and the estimators can be viewed as quasilikelihood estimators using the projected score functions on an adaptive basis and they are closely related to minimum chi-square estimators with random cells which can also be viewed as quasilikeliood estimators using a projected score functions on a special adaptive basis but the elements of such a basis were linearly dependent. A rule for selecting points on the nonnegative quadrant which make use of quasi Monte Carlo (QMC) numbers and two sample quantiles of the two marginal distributions is proposed if complete data is available and like minimum chi-square methods; the quadratic distance methods also offer chi-square statistics which appear to be useful in practice for model testing.